Search (175 results, page 1 of 9)

0.08185689 = product of:
  0.16371378 = sum of:
    0.08959906 = weight(_text_:data in 1605) [ClassicSimilarity], result of:
      0.08959906 = score(doc=1605,freq=24.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.60511017 = fieldWeight in 1605, product of:
          4.8989797 = tf(freq=24.0), with freq of:
            24.0 = termFreq=24.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1605)
    0.07411472 = sum of:
      0.042392377 = weight(_text_:processing in 1605) [ClassicSimilarity], result of:
        0.042392377 = score(doc=1605,freq=2.0), product of:
          0.18956426 = queryWeight, product of:
            4.048147 = idf(docFreq=2097, maxDocs=44218)
            0.046827413 = queryNorm
          0.22363065 = fieldWeight in 1605, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            4.048147 = idf(docFreq=2097, maxDocs=44218)
            0.0390625 = fieldNorm(doc=1605)
      0.03172234 = weight(_text_:22 in 1605) [ClassicSimilarity], result of:
        0.03172234 = score(doc=1605,freq=2.0), product of:
          0.16398162 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046827413 = queryNorm
          0.19345059 = fieldWeight in 1605, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0390625 = fieldNorm(doc=1605)
  0.5 = coord(2/4)

Abstract

Numerous studies have explored the possibility of uncovering information from web search queries but few have examined the factors that affect web query data sources. We conducted a study that investigated this issue by comparing Google Trends and Baidu Index. Data from these two services are based on queries entered by users into Google and Baidu, two of the largest search engines in the world. We first compared the features and functions of the two services based on documents and extensive testing. We then carried out an empirical study that collected query volume data from the two sources. We found that data from both sources could be used to predict the quality of Chinese universities and companies. Despite the differences between the two services in terms of technology, such as differing methods of language processing, the search volume data from the two were highly correlated and combining the two data sources did not improve the predictive power of the data. However, there was a major difference between the two in terms of data availability. Baidu Index was able to provide more search volume data than Google Trends did. Our analysis showed that the disadvantage of Google Trends in this regard was due to Google's smaller user base in China. The implication of this finding goes beyond China. Google's user bases in many countries are smaller than that in China, so the search volume data related to those countries could result in the same issue as that related to China.

Source

Journal of the Association for Information Science and Technology. 66(2015) no.1, S.13-22

Theme

Data Mining

0.07360171 = product of:
  0.14720342 = sum of:
    0.09580538 = weight(_text_:data in 1381) [ClassicSimilarity], result of:
      0.09580538 = score(doc=1381,freq=14.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.64702475 = fieldWeight in 1381, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0546875 = fieldNorm(doc=1381)
    0.05139803 = product of:
      0.10279606 = sum of:
        0.10279606 = weight(_text_:processing in 1381) [ClassicSimilarity], result of:
          0.10279606 = score(doc=1381,freq=6.0), product of:
            0.18956426 = queryWeight, product of:
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046827413 = queryNorm
            0.54227555 = fieldWeight in 1381, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1381)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

Neben den operativen Informationssystemen, welche die Abwicklung des betrieblichen Tagesgeschäftes unterstützen, treten heute verstärkt Informationssysteme für analytische Aufgaben der Fach- und Führungskräfte in den Vordergrund. In fast allen Unternehmen werden derzeit Begriffe und Konzepte wie Data Warehouse, On-Line Analytical Processing und Data Mining diskutiert und die zugehörigen Produkte evaluiert. Vor diesem Hintergrund zielt der vorliegende Sammelband darauf ab, einen aktuellen Überblick über Technologien, Produkte und Trends zu bieten. Als Entscheidungsgrundlage für den Praktiker beim Aufbau und Einsatz derartiger analytischer Informationssysteme können die unterschiedlichen Beiträge aus Wirtschaft und Wissenschaft wertvolle Hilfestellung leisten.

Content

Grundlagen.- Data Warehouse.- On-line Analytical Processing.- Data Mining.- Betriebswirtschaftliche und strategische Aspekte.

Theme

Data Mining

0.07135947 = product of:
  0.14271894 = sum of:
    0.112743005 = weight(_text_:data in 5053) [ClassicSimilarity], result of:
      0.112743005 = score(doc=5053,freq=38.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.7614136 = fieldWeight in 5053, product of:
          6.164414 = tf(freq=38.0), with freq of:
            38.0 = termFreq=38.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5053)
    0.029975938 = product of:
      0.059951875 = sum of:
        0.059951875 = weight(_text_:processing in 5053) [ClassicSimilarity], result of:
          0.059951875 = score(doc=5053,freq=4.0), product of:
            0.18956426 = queryWeight, product of:
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046827413 = queryNorm
            0.3162615 = fieldWeight in 5053, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5053)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

Big Data Analytics (BDA) is increasingly becoming a trending practice that generates an enormous amount of data and provides a new opportunity that is helpful in relevant decision-making. The developments in Big Data Analytics provide a new paradigm and solutions for big data sources, storage, and advanced analytics. The BDA provide a nuanced view of big data development, and insights on how it can truly create value for firm and customer. This article presents a comprehensive, well-informed examination, and realistic analysis of deploying big data analytics successfully in companies. It provides an overview of the architecture of BDA including six components, namely: (i) data generation, (ii) data acquisition, (iii) data storage, (iv) advanced data analytics, (v) data visualization, and (vi) decision-making for value-creation. In this paper, seven V's characteristics of BDA namely Volume, Velocity, Variety, Valence, Veracity, Variability, and Value are explored. The various big data analytics tools, techniques and technologies have been described. Furthermore, it presents a methodical analysis for the usage of Big Data Analytics in various applications such as agriculture, healthcare, cyber security, and smart city. This paper also highlights the previous research, challenges, current status, and future directions of big data analytics for various application platforms. This overview highlights three issues, namely (i) concepts, characteristics and processing paradigms of Big Data Analytics; (ii) the state-of-the-art framework for decision-making in BDA for companies to insight value-creation; and (iii) the current challenges of Big Data Analytics as well as possible future directions.

Footnote

Beitrag in einem Themenheft: 'In (Big) Data we trust: Value creation in knowledge organizations'.

Source

Information processing and management. 54(2018) no.5, S.758-790

Theme

Data Mining

0.070249505 = product of:
  0.14049901 = sum of:
    0.09253751 = weight(_text_:data in 1380) [ClassicSimilarity], result of:
      0.09253751 = score(doc=1380,freq=10.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.6249551 = fieldWeight in 1380, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0625 = fieldNorm(doc=1380)
    0.0479615 = product of:
      0.095923 = sum of:
        0.095923 = weight(_text_:processing in 1380) [ClassicSimilarity], result of:
          0.095923 = score(doc=1380,freq=4.0), product of:
            0.18956426 = queryWeight, product of:
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046827413 = queryNorm
            0.5060184 = fieldWeight in 1380, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.0625 = fieldNorm(doc=1380)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

Neben den operativen Informationssystemen treten heute verstärkt Informationssysteme für die analytischen Aufgaben der Fach- und Führungskräfte in den Vordergrund. In fast allen Unternehmen werden derzeit Begriffe und Konzepte wie Data Warehouse, On-Line Analytical Processing und Data Mining diskutiert und die zugehörigen Produkte evaluiert. Vor diesem Hintergrund zielt der vorliegende Sammelband darauf, einen aktuellen Überblick über Technologien, Produkte und Trends zu bieten. Als Entscheidungsgrundlage für den Praktiker beim Aufbau und Einsatz derartiger analytischer Informationssysteme können die unterschiedlichen Beiträge aus Wirtschaft und Wissenschaft wertvolle Hilfestellung leisten

Theme

Data Mining

0.06743487 = product of:
  0.13486974 = sum of:
    0.10949186 = weight(_text_:data in 4261) [ClassicSimilarity], result of:
      0.10949186 = score(doc=4261,freq=14.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.7394569 = fieldWeight in 4261, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0625 = fieldNorm(doc=4261)
    0.025377871 = product of:
      0.050755743 = sum of:
        0.050755743 = weight(_text_:22 in 4261) [ClassicSimilarity], result of:
          0.050755743 = score(doc=4261,freq=2.0), product of:
            0.16398162 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046827413 = queryNorm
            0.30952093 = fieldWeight in 4261, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0625 = fieldNorm(doc=4261)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Date

17. 7.2002 19:22:06

RSWK

Data-warehouse-Konzept / Lehrbuch
Data mining / Lehrbuch

Subject

Data-warehouse-Konzept / Lehrbuch
Data mining / Lehrbuch

Theme

Data Mining

0.0629278 = product of:
  0.1258556 = sum of:
    0.08778879 = weight(_text_:data in 6783) [ClassicSimilarity], result of:
      0.08778879 = score(doc=6783,freq=4.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.5928845 = fieldWeight in 6783, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.09375 = fieldNorm(doc=6783)
    0.038066804 = product of:
      0.07613361 = sum of:
        0.07613361 = weight(_text_:22 in 6783) [ClassicSimilarity], result of:
          0.07613361 = score(doc=6783,freq=2.0), product of:
            0.16398162 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046827413 = queryNorm
            0.46428138 = fieldWeight in 6783, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.09375 = fieldNorm(doc=6783)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Footnote

A special issue of selected papers from the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'97), held Singapore, 22-23 Feb 1997

Theme

Data Mining

0.059274744 = product of:
  0.11854949 = sum of:
    0.09311406 = weight(_text_:data in 3829) [ClassicSimilarity], result of:
      0.09311406 = score(doc=3829,freq=18.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.6288489 = fieldWeight in 3829, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046875 = fieldNorm(doc=3829)
    0.025435425 = product of:
      0.05087085 = sum of:
        0.05087085 = weight(_text_:processing in 3829) [ClassicSimilarity], result of:
          0.05087085 = score(doc=3829,freq=2.0), product of:
            0.18956426 = queryWeight, product of:
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046827413 = queryNorm
            0.26835677 = fieldWeight in 3829, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046875 = fieldNorm(doc=3829)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

Knowing what to do with the massive amount of data collected has always been an ongoing issue for many organizations. While data mining has been touted to be the solution, it has failed to deliver the impact despite its successes in many areas. One reason is that data mining algorithms were not designed for the real world, i.e., they usually assume a static view of the data and a stable execution environment where resourcesare abundant. The reality however is that data are constantly changing and the execution environment is dynamic. Hence, it becomes difficult for data mining to truly deliver timely and relevant results. Recently, the processing of stream data has received many attention. What is interesting is that the methodology to design stream-based algorithms may well be the solution to the above problem. In this entry, we discuss this issue and present an overview of recent works.

Theme

Data Mining

0.058416665 = product of:
  0.11683333 = sum of:
    0.07242205 = weight(_text_:data in 4577) [ClassicSimilarity], result of:
      0.07242205 = score(doc=4577,freq=2.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.48910472 = fieldWeight in 4577, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.109375 = fieldNorm(doc=4577)
    0.044411276 = product of:
      0.08882255 = sum of:
        0.08882255 = weight(_text_:22 in 4577) [ClassicSimilarity], result of:
          0.08882255 = score(doc=4577,freq=2.0), product of:
            0.16398162 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046827413 = queryNorm
            0.5416616 = fieldWeight in 4577, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.109375 = fieldNorm(doc=4577)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Date

2. 4.2000 18:01:22

Theme

Data Mining

0.05647345 = product of:
  0.1129469 = sum of:
    0.062076043 = weight(_text_:data in 4642) [ClassicSimilarity], result of:
      0.062076043 = score(doc=4642,freq=2.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.4192326 = fieldWeight in 4642, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.09375 = fieldNorm(doc=4642)
    0.05087085 = product of:
      0.1017417 = sum of:
        0.1017417 = weight(_text_:processing in 4642) [ClassicSimilarity], result of:
          0.1017417 = score(doc=4642,freq=2.0), product of:
            0.18956426 = queryWeight, product of:
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046827413 = queryNorm
            0.53671354 = fieldWeight in 4642, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.09375 = fieldNorm(doc=4642)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Theme

Data Mining

0.052730113 = product of:
  0.10546023 = sum of:
    0.08959906 = weight(_text_:data in 5011) [ClassicSimilarity], result of:
      0.08959906 = score(doc=5011,freq=24.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.60511017 = fieldWeight in 5011, product of:
          4.8989797 = tf(freq=24.0), with freq of:
            24.0 = termFreq=24.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5011)
    0.01586117 = product of:
      0.03172234 = sum of:
        0.03172234 = weight(_text_:22 in 5011) [ClassicSimilarity], result of:
          0.03172234 = score(doc=5011,freq=2.0), product of:
            0.16398162 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046827413 = queryNorm
            0.19345059 = fieldWeight in 5011, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5011)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

The present challenge faced by scientists working with Big Data comes in the overwhelming volume and level of detail provided by current data sets. Exceeding traditional empirical approaches, Big Data opens a new perspective on scientific work in which data comes to play a role in the development of the scientific problematic to be developed. Addressing this reconfiguration of our relationship with data through readings of Wittgenstein, Macherey, and Popper, we propose a picture of science that encourages scientists to engage with the data in a direct way, using the data itself as an instrument for scientific investigation. Using GIS as a theme, we develop the concept of cyber-human systems of thought and understanding to bridge the divide between representative (theoretical) thinking and (non-theoretical) data-driven science. At the foundation of these systems, we invoke the concept of the "semantic pixel" to establish a logical and virtual space linking data and the work of scientists. It is with this discussion of the relationship between analysts in their pursuit of knowledge and the rise of Big Data that this present discussion of the philosophical foundations of Big Data addresses the central questions raised by social informatics research.

Date

7. 3.2019 16:32:22

Theme

Data Mining

0.049204174 = product of:
  0.09840835 = sum of:
    0.06843241 = weight(_text_:data in 804) [ClassicSimilarity], result of:
      0.06843241 = score(doc=804,freq=14.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.46216056 = fieldWeight in 804, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0390625 = fieldNorm(doc=804)
    0.029975938 = product of:
      0.059951875 = sum of:
        0.059951875 = weight(_text_:processing in 804) [ClassicSimilarity], result of:
          0.059951875 = score(doc=804,freq=4.0), product of:
            0.18956426 = queryWeight, product of:
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046827413 = queryNorm
            0.3162615 = fieldWeight in 804, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.0390625 = fieldNorm(doc=804)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

Extracting content from text continues to be an important research problem for information processing and management. Approaches to capture the semantics of text-based document collections may be based on Bayesian models, probability theory, vector space models, statistical models, or even graph theory. As the volume of digitized textual media continues to grow, so does the need for designing robust, scalable indexing and search strategies (software) to meet a variety of user needs. Knowledge extraction or creation from text requires systematic yet reliable processing that can be codified and adapted for changing needs and environments. This book will draw upon experts in both academia and industry to recommend practical approaches to the purification, indexing, and mining of textual information. It will address document identification, clustering and categorizing documents, cleaning text, and visualizing semantic models of text.

Classification

ST 270 Informatik / Monographien / Software und -entwicklung / Datenbanken, Datenbanksysteme, Data base management, Informationssysteme

LCSH

Data mining ; Information retrieval
Data mining / Congresses (GBV)

RVK

ST 270 Informatik / Monographien / Software und -entwicklung / Datenbanken, Datenbanksysteme, Data base management, Informationssysteme

Subject

Data mining ; Information retrieval
Data mining / Congresses (GBV)

Theme

Data Mining

0.04852856 = product of:
  0.09705712 = sum of:
    0.07167925 = weight(_text_:data in 1737) [ClassicSimilarity], result of:
      0.07167925 = score(doc=1737,freq=6.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.48408815 = fieldWeight in 1737, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0625 = fieldNorm(doc=1737)
    0.025377871 = product of:
      0.050755743 = sum of:
        0.050755743 = weight(_text_:22 in 1737) [ClassicSimilarity], result of:
          0.050755743 = score(doc=1737,freq=2.0), product of:
            0.16398162 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046827413 = queryNorm
            0.30952093 = fieldWeight in 1737, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0625 = fieldNorm(doc=1737)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

Defines digital libraries and discusses the effects of new technology on librarians. Examines the different viewpoints of librarians and information technologists on digital libraries. Describes the development of a digital library at the National Drug Intelligence Center, USA, which was carried out in collaboration with information technology experts. The system is based on Web enabled search technology to find information, data visualization and data mining to visualize it and use of SGML as an information standard to store it

Date

22.11.1998 18:57:22

Theme

Data Mining

0.04753036 = product of:
  0.09506072 = sum of:
    0.07602732 = weight(_text_:data in 1383) [ClassicSimilarity], result of:
      0.07602732 = score(doc=1383,freq=12.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.513453 = fieldWeight in 1383, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046875 = fieldNorm(doc=1383)
    0.019033402 = product of:
      0.038066804 = sum of:
        0.038066804 = weight(_text_:22 in 1383) [ClassicSimilarity], result of:
          0.038066804 = score(doc=1383,freq=2.0), product of:
            0.16398162 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046827413 = queryNorm
            0.23214069 = fieldWeight in 1383, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=1383)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

Das Buch richtet sich an Praktiker in Unternehmen, die sich mit der Analyse von großen Datenbeständen beschäftigen. Nach einem kurzen Theorieteil werden vier Fallstudien aus dem Customer Relationship Management eines Versandhändlers bearbeitet. Dabei wurden acht führende Softwarelösungen verwendet: der Intelligent Miner von IBM, der Enterprise Miner von SAS, Clementine von SPSS, Knowledge Studio von Angoss, der Delta Miner von Bissantz, der Business Miner von Business Object und die Data Engine von MIT. Im Rahmen der Fallstudien werden die Stärken und Schwächen der einzelnen Lösungen deutlich, und die methodisch-korrekte Vorgehensweise beim Data Mining wird aufgezeigt. Beides liefert wertvolle Entscheidungshilfen für die Auswahl von Standardsoftware zum Data Mining und für die praktische Datenanalyse.

Content

Modelle, Methoden und Werkzeuge: Ziele und Aufbau der Untersuchung.- Grundlagen.- Planung und Entscheidung mit Data-Mining-Unterstützung.- Methoden.- Funktionalität und Handling der Softwarelösungen. Fallstudien: Ausgangssituation und Datenbestand im Versandhandel.- Kundensegmentierung.- Erklärung regionaler Marketingerfolge zur Neukundengewinnung.Prognose des Customer Lifetime Values.- Selektion von Kunden für eine Direktmarketingaktion.- Welche Softwarelösung für welche Entscheidung?- Fazit und Marktentwicklungen.

Date

22. 3.2008 14:46:06

Theme

Data Mining

0.047419276 = product of:
  0.09483855 = sum of:
    0.06940313 = weight(_text_:data in 2952) [ClassicSimilarity], result of:
      0.06940313 = score(doc=2952,freq=10.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.46871632 = fieldWeight in 2952, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046875 = fieldNorm(doc=2952)
    0.025435425 = product of:
      0.05087085 = sum of:
        0.05087085 = weight(_text_:processing in 2952) [ClassicSimilarity], result of:
          0.05087085 = score(doc=2952,freq=2.0), product of:
            0.18956426 = queryWeight, product of:
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046827413 = queryNorm
            0.26835677 = fieldWeight in 2952, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046875 = fieldNorm(doc=2952)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

The large amount of data collected today is quickly overwhelming researchers' abilities to interpret the data and discover interesting patterns. Knowledge discovery and data mining approaches hold the potential to automate the interpretation process, but these approaches frequently utilize computationally expensive algorithms. In particular, scientific discovery systems focus on the utilization of richer data representation, sometimes without regard for scalability. This research investigates approaches for scaling a particular knowledge discovery in databases (KDD) system, SUBDUE, using parallel and distributed resources. SUBDUE has been used to discover interesting and repetitive concepts in graph-based databases from a variety of domains, but requires a substantial amount of processing time. Experiments that demonstrate scalability of parallel versions of the SUBDUE system are performed using CAD circuit databases and artificially-generated databases, and potential achievements and obstacles are discussed

Theme

Data Mining

0.04727441 = product of:
  0.09454882 = sum of:
    0.057835944 = weight(_text_:data in 4367) [ClassicSimilarity], result of:
      0.057835944 = score(doc=4367,freq=10.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.39059696 = fieldWeight in 4367, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4367)
    0.036712877 = product of:
      0.073425755 = sum of:
        0.073425755 = weight(_text_:processing in 4367) [ClassicSimilarity], result of:
          0.073425755 = score(doc=4367,freq=6.0), product of:
            0.18956426 = queryWeight, product of:
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046827413 = queryNorm
            0.38733965 = fieldWeight in 4367, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4367)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

Information extraction is an important text-mining task that aims at extracting prespecified types of information from large text collections and making them available in structured representations such as databases. In the biomedical domain, information extraction can be applied to help biologists make the most use of their digital-literature archives. Currently, there are large amounts of biomedical literature that contain rich information about biomedical substances. Extracting such knowledge requires a good named entity recognition technique. In this article, we combine conditional random fields (CRFs), a state-of-the-art sequence-labeling algorithm, with two semisupervised learning techniques, bootstrapping and feature sampling, to recognize disease names from biomedical literature. Two data-processing strategies for each technique also were analyzed: one sequentially processing unlabeled data partitions and another one processing unlabeled data partitions in a round-robin fashion. The experimental results showed the advantage of semisupervised learning techniques given limited labeled training data. Specifically, CRFs with bootstrapping implemented in sequential fashion outperformed strictly supervised CRFs for disease name recognition. The project was supported by NIH/NLM Grant R33 LM07299-01, 2002-2005.

Theme

Data Mining

0.04718969 = product of:
  0.09437938 = sum of:
    0.07742243 = weight(_text_:data in 354) [ClassicSimilarity], result of:
      0.07742243 = score(doc=354,freq=28.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.52287495 = fieldWeight in 354, product of:
          5.2915025 = tf(freq=28.0), with freq of:
            28.0 = termFreq=28.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.03125 = fieldNorm(doc=354)
    0.016956951 = product of:
      0.033913903 = sum of:
        0.033913903 = weight(_text_:processing in 354) [ClassicSimilarity], result of:
          0.033913903 = score(doc=354,freq=2.0), product of:
            0.18956426 = queryWeight, product of:
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046827413 = queryNorm
            0.17890452 = fieldWeight in 354, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.03125 = fieldNorm(doc=354)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

Web mining aims to discover useful information and knowledge from the Web hyperlink structure, page contents, and usage data. Although Web mining uses many conventional data mining techniques, it is not purely an application of traditional data mining due to the semistructured and unstructured nature of the Web data and its heterogeneity. It has also developed many of its own algorithms and techniques. Liu has written a comprehensive text on Web data mining. Key topics of structure mining, content mining, and usage mining are covered both in breadth and in depth. His book brings together all the essential concepts and algorithms from related areas such as data mining, machine learning, and text processing to form an authoritative and coherent text. The book offers a rich blend of theory and practice, addressing seminal research ideas, as well as examining the technology from a practical point of view. It is suitable for students, researchers and practitioners interested in Web mining both as a learning text and a reference book. Lecturers can readily use it for classes on data mining, Web mining, and Web search. Additional teaching materials such as lecture slides, datasets, and implemented algorithms are available online.

Content

Inhalt: 1. Introduction 2. Association Rules and Sequential Patterns 3. Supervised Learning 4. Unsupervised Learning 5. Partially Supervised Learning 6. Information Retrieval and Web Search 7. Social Network Analysis 8. Web Crawling 9. Structured Data Extraction: Wrapper Generation 10. Information Integration

RSWK

World Wide Web / Data Mining

Series

Data-centric systems and applications

Subject

World Wide Web / Data Mining

Theme

Data Mining

0.0448143 = product of:
  0.0896286 = sum of:
    0.06843241 = weight(_text_:data in 5997) [ClassicSimilarity], result of:
      0.06843241 = score(doc=5997,freq=14.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.46216056 = fieldWeight in 5997, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5997)
    0.021196188 = product of:
      0.042392377 = sum of:
        0.042392377 = weight(_text_:processing in 5997) [ClassicSimilarity], result of:
          0.042392377 = score(doc=5997,freq=2.0), product of:
            0.18956426 = queryWeight, product of:
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046827413 = queryNorm
            0.22363065 = fieldWeight in 5997, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5997)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

Given the huge amount of information in the internet and in practically every domain of knowledge that we are facing today, knowledge discovery calls for automation. The book deals with methods from classification and data analysis that respond effectively to this rapidly growing challenge. The interested reader will find new methodological insights as well as applications in economics, management science, finance, and marketing, and in pattern recognition, biology, health, and archaeology.

Content

Data Analysis, Statistics, and Classification.- Pattern Recognition and Automation.- Data Mining, Information Processing, and Automation.- New Media, Web Mining, and Automation.- Applications in Management Science, Finance, and Marketing.- Applications in Medicine, Biology, Archaeology, and Others.- Author Index.- Subject Index.

RSWK

Data Mining / Kongress / Passau <2000>

Series

Proceedings of the ... annual conference of the Gesellschaft für Klassifikation e.V. ; 24)(Studies in classification, data analysis, and knowledge organization

Subject

Data Mining / Kongress / Passau <2000>

Theme

Data Mining

0.041951865 = product of:
  0.08390373 = sum of:
    0.058525857 = weight(_text_:data in 1270) [ClassicSimilarity], result of:
      0.058525857 = score(doc=1270,freq=4.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.3952563 = fieldWeight in 1270, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0625 = fieldNorm(doc=1270)
    0.025377871 = product of:
      0.050755743 = sum of:
        0.050755743 = weight(_text_:22 in 1270) [ClassicSimilarity], result of:
          0.050755743 = score(doc=1270,freq=2.0), product of:
            0.16398162 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046827413 = queryNorm
            0.30952093 = fieldWeight in 1270, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0625 = fieldNorm(doc=1270)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

Current algorithms for finding associations among the attributes describing data in a database have a number of shortcomings. Presents a novel method for association generation, that answers all desiderata. The method is different from all existing algorithms and especially suitable to textual databases with binary attributes. Uses subword trees for quick indexing into the required database statistics. Tests the algorithm on the Reuters-22173 database with satisfactory results

Source

Information systems. 22(1997) nos.5/6, S.333-347

Theme

Data Mining

0.03993276 = product of:
  0.07986552 = sum of:
    0.043894395 = weight(_text_:data in 4716) [ClassicSimilarity], result of:
      0.043894395 = score(doc=4716,freq=4.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.29644224 = fieldWeight in 4716, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046875 = fieldNorm(doc=4716)
    0.035971127 = product of:
      0.071942255 = sum of:
        0.071942255 = weight(_text_:processing in 4716) [ClassicSimilarity], result of:
          0.071942255 = score(doc=4716,freq=4.0), product of:
            0.18956426 = queryWeight, product of:
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046827413 = queryNorm
            0.3795138 = fieldWeight in 4716, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046875 = fieldNorm(doc=4716)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

In this paper we give a synoptic view of the growth of the text processing technology of informatione xtraction (IE) whose function is to extract information about a pre-specified set of entities, relations or events from natural language texts and to record this information in structured representations called templates. Here we describe the nature of the IE task, review the history of the area from its origins in AI work in the 1960s and 70s till the present, discuss the techniques being used to carry out the task, describe application areas where IE systems are or are about to be at work, and conclude with a discussion of the challenges facing the area. What emerges is a picture of an exciting new text processing technology with a host of new applications, both on its own and in conjunction with other technologies, such as information retrieval, machine translation and data mining

Theme

Data Mining

0.039750893 = product of:
  0.079501785 = sum of:
    0.07315732 = weight(_text_:data in 1789) [ClassicSimilarity], result of:
      0.07315732 = score(doc=1789,freq=100.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.49407038 = fieldWeight in 1789, product of:
          10.0 = tf(freq=100.0), with freq of:
            100.0 = termFreq=100.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.015625 = fieldNorm(doc=1789)
    0.006344468 = product of:
      0.012688936 = sum of:
        0.012688936 = weight(_text_:22 in 1789) [ClassicSimilarity], result of:
          0.012688936 = score(doc=1789,freq=2.0), product of:
            0.16398162 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046827413 = queryNorm
            0.07738023 = fieldWeight in 1789, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.015625 = fieldNorm(doc=1789)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Date

23. 3.2008 19:10:22

Footnote

Rez. in: JASIST 54(2003) no.9, S.905-906 (C.A. Badurek): "Visual approaches for knowledge discovery in very large databases are a prime research need for information scientists focused an extracting meaningful information from the ever growing stores of data from a variety of domains, including business, the geosciences, and satellite and medical imagery. This work presents a summary of research efforts in the fields of data mining, knowledge discovery, and data visualization with the goal of aiding the integration of research approaches and techniques from these major fields. The editors, leading computer scientists from academia and industry, present a collection of 32 papers from contributors who are incorporating visualization and data mining techniques through academic research as well application development in industry and government agencies. Information Visualization focuses upon techniques to enhance the natural abilities of humans to visually understand data, in particular, large-scale data sets. It is primarily concerned with developing interactive graphical representations to enable users to more intuitively make sense of multidimensional data as part of the data exploration process. It includes research from computer science, psychology, human-computer interaction, statistics, and information science. Knowledge Discovery in Databases (KDD) most often refers to the process of mining databases for previously unknown patterns and trends in data. Data mining refers to the particular computational methods or algorithms used in this process. The data mining research field is most related to computational advances in database theory, artificial intelligence and machine learning. This work compiles research summaries from these main research areas in order to provide "a reference work containing the collection of thoughts and ideas of noted researchers from the fields of data mining and data visualization" (p. 8). It addresses these areas in three main sections: the first an data visualization, the second an KDD and model visualization, and the last an using visualization in the knowledge discovery process. The seven chapters of Part One focus upon methodologies and successful techniques from the field of Data Visualization. Hoffman and Grinstein (Chapter 2) give a particularly good overview of the field of data visualization and its potential application to data mining. An introduction to the terminology of data visualization, relation to perceptual and cognitive science, and discussion of the major visualization display techniques are presented. Discussion and illustration explain the usefulness and proper context of such data visualization techniques as scatter plots, 2D and 3D isosurfaces, glyphs, parallel coordinates, and radial coordinate visualizations. Remaining chapters present the need for standardization of visualization methods, discussion of user requirements in the development of tools, and examples of using information visualization in addressing research problems.
In 13 chapters, Part Two provides an introduction to KDD, an overview of data mining techniques, and examples of the usefulness of data model visualizations. The importance of visualization throughout the KDD process is stressed in many of the chapters. In particular, the need for measures of visualization effectiveness, benchmarking for identifying best practices, and the use of standardized sample data sets is convincingly presented. Many of the important data mining approaches are discussed in this complementary context. Cluster and outlier detection, classification techniques, and rule discovery algorithms are presented as the basic techniques common to the KDD process. The potential effectiveness of using visualization in the data modeling process are illustrated in chapters focused an using visualization for helping users understand the KDD process, ask questions and form hypotheses about their data, and evaluate the accuracy and veracity of their results. The 11 chapters of Part Three provide an overview of the KDD process and successful approaches to integrating KDD, data mining, and visualization in complementary domains. Rhodes (Chapter 21) begins this section with an excellent overview of the relation between the KDD process and data mining techniques. He states that the "primary goals of data mining are to describe the existing data and to predict the behavior or characteristics of future data of the same type" (p. 281). These goals are met by data mining tasks such as classification, regression, clustering, summarization, dependency modeling, and change or deviation detection. Subsequent chapters demonstrate how visualization can aid users in the interactive process of knowledge discovery by graphically representing the results from these iterative tasks. Finally, examples of the usefulness of integrating visualization and data mining tools in the domain of business, imagery and text mining, and massive data sets are provided. This text concludes with a thorough and useful 17-page index and lengthy yet integrating 17-page summary of the academic and industrial backgrounds of the contributing authors. A 16-page set of color inserts provide a better representation of the visualizations discussed, and a URL provided suggests that readers may view all the book's figures in color on-line, although as of this submission date it only provides access to a summary of the book and its contents. The overall contribution of this work is its focus an bridging two distinct areas of research, making it a valuable addition to the Morgan Kaufmann Series in Database Management Systems. The editors of this text have met their main goal of providing the first textbook integrating knowledge discovery, data mining, and visualization. Although it contributes greatly to our under- standing of the development and current state of the field, a major weakness of this text is that there is no concluding chapter to discuss the contributions of the sum of these contributed papers or give direction to possible future areas of research. "Integration of expertise between two different disciplines is a difficult process of communication and reeducation. Integrating data mining and visualization is particularly complex because each of these fields in itself must draw an a wide range of research experience" (p. 300). Although this work contributes to the crossdisciplinary communication needed to advance visualization in KDD, a more formal call for an interdisciplinary research agenda in a concluding chapter would have provided a more satisfying conclusion to a very good introductory text.
With contributors almost exclusively from the computer science field, the intended audience of this work is heavily slanted towards a computer science perspective. However, it is highly readable and provides introductory material that would be useful to information scientists from a variety of domains. Yet, much interesting work in information visualization from other fields could have been included giving the work more of an interdisciplinary perspective to complement their goals of integrating work in this area. Unfortunately, many of the application chapters are these, shallow, and lack complementary illustrations of visualization techniques or user interfaces used. However, they do provide insight into the many applications being developed in this rapidly expanding field. The authors have successfully put together a highly useful reference text for the data mining and information visualization communities. Those interested in a good introduction and overview of complementary research areas in these fields will be satisfied with this collection of papers. The focus upon integrating data visualization with data mining complements texts in each of these fields, such as Advances in Knowledge Discovery and Data Mining (Fayyad et al., MIT Press) and Readings in Information Visualization: Using Vision to Think (Card et. al., Morgan Kauffman). This unique work is a good starting point for future interaction between researchers in the fields of data visualization and data mining and makes a good accompaniment for a course focused an integrating these areas or to the main reference texts in these fields."

LCSH

Data mining

RSWK

Visualisierung / Computergraphik / Data Mining
Data Mining / Visualisierung / Aufsatzsammlung (BVB)

Series

Morgan Kaufmann series in data management systems

Subject

Visualisierung / Computergraphik / Data Mining
Data Mining / Visualisierung / Aufsatzsammlung (BVB)
Data mining

Theme

Data Mining