Search (137 results, page 1 of 7)

0.01742949 = product of:
  0.052288465 = sum of:
    0.020155499 = weight(_text_:library in 1737) [ClassicSimilarity], result of:
      0.020155499 = score(doc=1737,freq=2.0), product of:
        0.08672522 = queryWeight, product of:
          2.6293786 = idf(docFreq=8668, maxDocs=44218)
          0.03298316 = queryNorm
        0.23240642 = fieldWeight in 1737, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.6293786 = idf(docFreq=8668, maxDocs=44218)
          0.0625 = fieldNorm(doc=1737)
    0.014257914 = weight(_text_:of in 1737) [ClassicSimilarity], result of:
      0.014257914 = score(doc=1737,freq=8.0), product of:
        0.05157766 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03298316 = queryNorm
        0.27643585 = fieldWeight in 1737, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0625 = fieldNorm(doc=1737)
    0.017875053 = product of:
      0.035750106 = sum of:
        0.035750106 = weight(_text_:22 in 1737) [ClassicSimilarity], result of:
          0.035750106 = score(doc=1737,freq=2.0), product of:
            0.11550141 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03298316 = 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)
  0.33333334 = coord(3/9)

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

0.016288018 = product of:
  0.04886405 = sum of:
    0.015116624 = weight(_text_:library in 473) [ClassicSimilarity], result of:
      0.015116624 = score(doc=473,freq=2.0), product of:
        0.08672522 = queryWeight, product of:
          2.6293786 = idf(docFreq=8668, maxDocs=44218)
          0.03298316 = queryNorm
        0.17430481 = fieldWeight in 473, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.6293786 = idf(docFreq=8668, maxDocs=44218)
          0.046875 = fieldNorm(doc=473)
    0.015122802 = weight(_text_:of in 473) [ClassicSimilarity], result of:
      0.015122802 = score(doc=473,freq=16.0), product of:
        0.05157766 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03298316 = queryNorm
        0.2932045 = fieldWeight in 473, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=473)
    0.018624624 = product of:
      0.03724925 = sum of:
        0.03724925 = weight(_text_:problems in 473) [ClassicSimilarity], result of:
          0.03724925 = score(doc=473,freq=2.0), product of:
            0.13613719 = queryWeight, product of:
              4.1274753 = idf(docFreq=1937, maxDocs=44218)
              0.03298316 = queryNorm
            0.27361554 = fieldWeight in 473, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.1274753 = idf(docFreq=1937, maxDocs=44218)
              0.046875 = fieldNorm(doc=473)
      0.5 = coord(1/2)
  0.33333334 = coord(3/9)

Abstract

The emergence of large multi-institutional digital libraries has opened the door to aggregate-level examinations of the published word. Such large-scale analysis offers a new way to pursue traditional problems in the humanities and social sciences, using digital methods to ask routine questions of large corpora. However, inquiry into multiple centuries of books is constrained by the burdens of scale, where statistical inference is technically complex and limited by hurdles to access and flexibility. This work examines the role that exploratory data analysis and visualization tools may play in understanding large bibliographic datasets. We present one such tool, HathiTrust+Bookworm, which allows multifaceted exploration of the multimillion work HathiTrust Digital Library, and center it in the broader space of scholarly tools for exploratory data analysis.

Source

Journal of the Association for Information Science and Technology. 73(2022) no.2, S.317-332

0.014789658 = product of:
  0.06655346 = sum of:
    0.03527212 = weight(_text_:library in 4577) [ClassicSimilarity], result of:
      0.03527212 = score(doc=4577,freq=2.0), product of:
        0.08672522 = queryWeight, product of:
          2.6293786 = idf(docFreq=8668, maxDocs=44218)
          0.03298316 = queryNorm
        0.40671125 = fieldWeight in 4577, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.6293786 = idf(docFreq=8668, maxDocs=44218)
          0.109375 = fieldNorm(doc=4577)
    0.03128134 = product of:
      0.06256268 = sum of:
        0.06256268 = weight(_text_:22 in 4577) [ClassicSimilarity], result of:
          0.06256268 = score(doc=4577,freq=2.0), product of:
            0.11550141 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03298316 = 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)
  0.22222222 = coord(2/9)

Date

2. 4.2000 18:01:22

Source

Library trends. 48(1999) no.1, S.182-208

0.01282392 = product of:
  0.057707638 = sum of:
    0.04242812 = product of:
      0.08485624 = sum of:
        0.08485624 = weight(_text_:headings in 5481) [ClassicSimilarity], result of:
          0.08485624 = score(doc=5481,freq=4.0), product of:
            0.15996648 = queryWeight, product of:
              4.849944 = idf(docFreq=940, maxDocs=44218)
              0.03298316 = queryNorm
            0.5304626 = fieldWeight in 5481, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.849944 = idf(docFreq=940, maxDocs=44218)
              0.0546875 = fieldNorm(doc=5481)
      0.5 = coord(1/2)
    0.015279518 = weight(_text_:of in 5481) [ClassicSimilarity], result of:
      0.015279518 = score(doc=5481,freq=12.0), product of:
        0.05157766 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03298316 = queryNorm
        0.29624295 = fieldWeight in 5481, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0546875 = fieldNorm(doc=5481)
  0.22222222 = coord(2/9)

Abstract

This article describes multiple experiments in text mining at Northern Illinois University that were undertaken to improve the efficiency and accuracy of cataloging. It focuses narrowly on subject analysis of dime novels, a format of inexpensive fiction that was popular in the United States between 1860 and 1915. NIU holds more than 55,000 dime novels in its collections, which it is in the process of comprehensively digitizing. Classification, keyword extraction, named-entity recognition, clustering, and topic modeling are discussed as means of assigning subject headings to improve their discoverability by researchers and to increase the productivity of digitization workflows.

0.012236238 = product of:
  0.055063073 = sum of:
    0.01764327 = weight(_text_:of in 4260) [ClassicSimilarity], result of:
      0.01764327 = score(doc=4260,freq=16.0), product of:
        0.05157766 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03298316 = queryNorm
        0.34207192 = fieldWeight in 4260, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0546875 = fieldNorm(doc=4260)
    0.037419803 = product of:
      0.07483961 = sum of:
        0.07483961 = weight(_text_:etc in 4260) [ClassicSimilarity], result of:
          0.07483961 = score(doc=4260,freq=2.0), product of:
            0.17865302 = queryWeight, product of:
              5.4164915 = idf(docFreq=533, maxDocs=44218)
              0.03298316 = queryNorm
            0.41891038 = fieldWeight in 4260, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.4164915 = idf(docFreq=533, maxDocs=44218)
              0.0546875 = fieldNorm(doc=4260)
      0.5 = coord(1/2)
  0.22222222 = coord(2/9)

Abstract

The volume presents recent developments in the introduction of fuzzy, probabilistic and rough elements into basic components of fuzzy databases, and their use (notably querying and information retrieval), from the point of view of data mining and knowledge discovery. The main novel aspect of the volume is that issues related to the use of fuzzy elements in databases, database querying, information retrieval, etc. are presented and discussed from the point of view, and for the purpose of data mining and knowledge discovery that are 'hot topics' in recent years

0.01163057 = product of:
  0.052337565 = sum of:
    0.021608504 = weight(_text_:of in 2338) [ClassicSimilarity], result of:
      0.021608504 = score(doc=2338,freq=24.0), product of:
        0.05157766 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03298316 = queryNorm
        0.41895083 = fieldWeight in 2338, product of:
          4.8989797 = tf(freq=24.0), with freq of:
            24.0 = termFreq=24.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0546875 = fieldNorm(doc=2338)
    0.030729063 = product of:
      0.061458126 = sum of:
        0.061458126 = weight(_text_:problems in 2338) [ClassicSimilarity], result of:
          0.061458126 = score(doc=2338,freq=4.0), product of:
            0.13613719 = queryWeight, product of:
              4.1274753 = idf(docFreq=1937, maxDocs=44218)
              0.03298316 = queryNorm
            0.4514426 = fieldWeight in 2338, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.1274753 = idf(docFreq=1937, maxDocs=44218)
              0.0546875 = fieldNorm(doc=2338)
      0.5 = coord(1/2)
  0.22222222 = coord(2/9)

Abstract

Hundreds of thousands of hashtags are generated every day on Twitter. Only a few will burst and become trending topics. In this article, we provide the definition of a bursting hashtag and conduct a systematic study of a series of challenging prediction problems that span the entire life cycles of bursting hashtags. Around the problem of "how to build a system to predict bursting hashtags," we explore different types of features and present machine learning solutions. On real data sets from Twitter, experiments are conducted to evaluate the effectiveness of the proposed solutions and the contributions of features.

Source

Journal of the Association for Information Science and Technology. 66(2015) no.12, S.2566-2579

0.010026095 = product of:
  0.04511743 = sum of:
    0.033329025 = weight(_text_:library in 2533) [ClassicSimilarity], result of:
      0.033329025 = score(doc=2533,freq=14.0), product of:
        0.08672522 = queryWeight, product of:
          2.6293786 = idf(docFreq=8668, maxDocs=44218)
          0.03298316 = queryNorm
        0.384306 = fieldWeight in 2533, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          2.6293786 = idf(docFreq=8668, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2533)
    0.0117884055 = weight(_text_:of in 2533) [ClassicSimilarity], result of:
      0.0117884055 = score(doc=2533,freq=14.0), product of:
        0.05157766 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03298316 = queryNorm
        0.22855641 = fieldWeight in 2533, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2533)
  0.22222222 = coord(2/9)

Abstract

Purpose - Since library storage has been increasing day by day, it is difficult for readers to find the books which interest them as well as representative booklists. How to utilize meaningful information effectively to improve the service quality of the digital library appears to be very important. The purpose of this paper is to provide a recommendation system architecture to promote digital library services in electronic libraries. Design/methodology/approach - In the proposed architecture, a two-phase data mining process used by association rule and clustering methods is designed to generate a recommendation system. The process considers not only the relationship of a cluster of users but also the associations among the information accessed. Findings - The process considered not only the relationship of a cluster of users but also the associations among the information accessed. With the advanced filter, the recommendation supported by the proposed system architecture would be closely served to meet users' needs. Originality/value - This paper not only constructs a recommendation service for readers to search books from the web but takes the initiative in finding the most suitable books for readers as well. Furthermore, library managers are expected to purchase core and hot books from a limited budget to maintain and satisfy the requirements of readers along with promoting digital library services.

Source

Electronic library. 25(2007) no.6, S.711-724

0.00939892 = product of:
  0.042295143 = sum of:
    0.017462308 = weight(_text_:of in 6625) [ClassicSimilarity], result of:
      0.017462308 = score(doc=6625,freq=12.0), product of:
        0.05157766 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03298316 = queryNorm
        0.33856338 = fieldWeight in 6625, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0625 = fieldNorm(doc=6625)
    0.024832834 = product of:
      0.049665667 = sum of:
        0.049665667 = weight(_text_:problems in 6625) [ClassicSimilarity], result of:
          0.049665667 = score(doc=6625,freq=2.0), product of:
            0.13613719 = queryWeight, product of:
              4.1274753 = idf(docFreq=1937, maxDocs=44218)
              0.03298316 = queryNorm
            0.36482072 = fieldWeight in 6625, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.1274753 = idf(docFreq=1937, maxDocs=44218)
              0.0625 = fieldNorm(doc=6625)
      0.5 = coord(1/2)
  0.22222222 = coord(2/9)

Abstract

Offers a data mining approach based on machine learning classification methods to the problem of automated cataloguing of online databases of digital images resulting from sky surveys. The SKICAT system automates the reduction and analysis of 3 terabytes of images expected to contain about 2 billion sky objects. It offers a solution to problems associated with the analysis of large data sets in science

0.009229992 = product of:
  0.04153496 = sum of:
    0.028168166 = weight(_text_:library in 1867) [ClassicSimilarity], result of:
      0.028168166 = score(doc=1867,freq=10.0), product of:
        0.08672522 = queryWeight, product of:
          2.6293786 = idf(docFreq=8668, maxDocs=44218)
          0.03298316 = queryNorm
        0.32479787 = fieldWeight in 1867, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          2.6293786 = idf(docFreq=8668, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1867)
    0.013366793 = weight(_text_:of in 1867) [ClassicSimilarity], result of:
      0.013366793 = score(doc=1867,freq=18.0), product of:
        0.05157766 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03298316 = queryNorm
        0.25915858 = fieldWeight in 1867, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1867)
  0.22222222 = coord(2/9)

Abstract

This research creates an intelligent agent for automated collection development in a digital library setting. It uses a predictive model based an facets of each Web page to select scholarly works. The criteria came from the academic library selection literature, and a Delphi study was used to refine the list to 41 criteria. A Perl program was designed to analyze a Web page for each criterion and applied to a large collection of scholarly and nonscholarly Web pages. Bibliomining, or data mining for libraries, was then used to create different classification models. Four techniques were used: logistic regression, nonparametric discriminant analysis, classification trees, and neural networks. Accuracy and return were used to judge the effectiveness of each model an test datasets. In addition, a set of problematic pages that were difficult to classify because of their similarity to scholarly research was gathered and classified using the models. The resulting models could be used in the selection process to automatically create a digital library of Webbased scholarly research works. In addition, the technique can be extended to create a digital library of any type of structured electronic information.

Source

Journal of the American Society for Information Science and technology. 54(2003) no.12, S.1081-1090

0.00876354 = product of:
  0.039435927 = sum of:
    0.01309673 = weight(_text_:of in 3884) [ClassicSimilarity], result of:
      0.01309673 = score(doc=3884,freq=12.0), product of:
        0.05157766 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03298316 = queryNorm
        0.25392252 = fieldWeight in 3884, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=3884)
    0.026339196 = product of:
      0.05267839 = sum of:
        0.05267839 = weight(_text_:problems in 3884) [ClassicSimilarity], result of:
          0.05267839 = score(doc=3884,freq=4.0), product of:
            0.13613719 = queryWeight, product of:
              4.1274753 = idf(docFreq=1937, maxDocs=44218)
              0.03298316 = queryNorm
            0.3869508 = fieldWeight in 3884, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.1274753 = idf(docFreq=1937, maxDocs=44218)
              0.046875 = fieldNorm(doc=3884)
      0.5 = coord(1/2)
  0.22222222 = coord(2/9)

Abstract

Techniques for multidimensional scaling visualize objects as points in a low-dimensional metric map. As a result, the visualizations are subject to the fundamental limitations of metric spaces. These limitations prevent multidimensional scaling from faithfully representing non-metric similarity data such as word associations or event co-occurrences. In particular, multidimensional scaling cannot faithfully represent intransitive pairwise similarities in a visualization, and it cannot faithfully visualize "central" objects. In this paper, we present an extension of a recently proposed multidimensional scaling technique called t-SNE. The extension aims to address the problems of traditional multidimensional scaling techniques when these techniques are used to visualize non-metric similarities. The new technique, called multiple maps t-SNE, alleviates these problems by constructing a collection of maps that reveal complementary structure in the similarity data. We apply multiple maps t-SNE to a large data set of word association data and to a data set of NIPS co-authorships, demonstrating its ability to successfully visualize non-metric similarities.

0.008703873 = product of:
  0.026111618 = sum of:
    0.015434646 = weight(_text_:of in 1789) [ClassicSimilarity], result of:
      0.015434646 = score(doc=1789,freq=150.0), product of:
        0.05157766 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03298316 = queryNorm
        0.2992506 = fieldWeight in 1789, product of:
          12.247449 = tf(freq=150.0), with freq of:
            150.0 = termFreq=150.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.015625 = fieldNorm(doc=1789)
    0.0062082084 = product of:
      0.012416417 = sum of:
        0.012416417 = weight(_text_:problems in 1789) [ClassicSimilarity], result of:
          0.012416417 = score(doc=1789,freq=2.0), product of:
            0.13613719 = queryWeight, product of:
              4.1274753 = idf(docFreq=1937, maxDocs=44218)
              0.03298316 = queryNorm
            0.09120518 = fieldWeight in 1789, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.1274753 = idf(docFreq=1937, maxDocs=44218)
              0.015625 = fieldNorm(doc=1789)
      0.5 = coord(1/2)
    0.0044687632 = product of:
      0.0089375265 = sum of:
        0.0089375265 = weight(_text_:22 in 1789) [ClassicSimilarity], result of:
          0.0089375265 = score(doc=1789,freq=2.0), product of:
            0.11550141 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03298316 = 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)
  0.33333334 = coord(3/9)

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."

0.008582368 = product of:
  0.038620654 = sum of:
    0.016671322 = weight(_text_:of in 604) [ClassicSimilarity], result of:
      0.016671322 = score(doc=604,freq=28.0), product of:
        0.05157766 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03298316 = queryNorm
        0.32322758 = fieldWeight in 604, product of:
          5.2915025 = tf(freq=28.0), with freq of:
            28.0 = termFreq=28.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=604)
    0.021949332 = product of:
      0.043898664 = sum of:
        0.043898664 = weight(_text_:problems in 604) [ClassicSimilarity], result of:
          0.043898664 = score(doc=604,freq=4.0), product of:
            0.13613719 = queryWeight, product of:
              4.1274753 = idf(docFreq=1937, maxDocs=44218)
              0.03298316 = queryNorm
            0.322459 = fieldWeight in 604, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.1274753 = idf(docFreq=1937, maxDocs=44218)
              0.0390625 = fieldNorm(doc=604)
      0.5 = coord(1/2)
  0.22222222 = coord(2/9)

Abstract

Modern information retrieval systems use keywords within documents as indexing terms for search of relevant documents. As Chinese is an ideographic character-based language, the words in the texts are not delimited by white spaces. Indexing of Chinese documents is impossible without a proper segmentation algorithm. Many Chinese segmentation algorithms have been proposed in the past. Traditional segmentation algorithms cannot operate without a large dictionary or a large corpus of training data. Nowadays, the Web has become the largest corpus that is ideal for Chinese segmentation. Although most search engines have problems in segmenting texts into proper words, they maintain huge databases of documents and frequencies of character sequences in the documents. Their databases are important potential resources for segmentation. In this paper, we propose a segmentation algorithm by mining Web data with the help of search engines. On the other hand, the Romanized pinyin of Chinese language indicates boundaries of words in the text. Our algorithm is the first to utilize the Romanized pinyin to segmentation. It is the first unified segmentation algorithm for the Chinese language from different geographical areas, and it is also domain independent because of the nature of the Web. Experiments have been conducted on the datasets of a recent Chinese segmentation competition. The results show that our algorithm outperforms the traditional algorithms in terms of precision and recall. Moreover, our algorithm can effectively deal with the problems of segmentation ambiguity, new word (unknown word) detection, and stop words.

Source

Journal of the American Society for Information Science and Technology. 58(2007) no.12, S.1820-1837

0.00849611 = product of:
  0.038232498 = sum of:
    0.016503768 = weight(_text_:of in 2899) [ClassicSimilarity], result of:
      0.016503768 = score(doc=2899,freq=14.0), product of:
        0.05157766 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03298316 = queryNorm
        0.31997898 = fieldWeight in 2899, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0546875 = fieldNorm(doc=2899)
    0.02172873 = product of:
      0.04345746 = sum of:
        0.04345746 = weight(_text_:problems in 2899) [ClassicSimilarity], result of:
          0.04345746 = score(doc=2899,freq=2.0), product of:
            0.13613719 = queryWeight, product of:
              4.1274753 = idf(docFreq=1937, maxDocs=44218)
              0.03298316 = queryNorm
            0.31921813 = fieldWeight in 2899, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.1274753 = idf(docFreq=1937, maxDocs=44218)
              0.0546875 = fieldNorm(doc=2899)
      0.5 = coord(1/2)
  0.22222222 = coord(2/9)

Abstract

Defines knowledge discovery and database mining. The challenge for knowledge discovery in databases (KDD) is to automatically process large quantities of raw data, identifying the most significant and meaningful patterns, and present these as as knowledge appropriate for achieving a user's goals. Data mining is the process of deriving useful knowledge from real world databases through the application of pattern extraction techniques. Explains the goals of, and motivation for, research work on data mining. Discusses the nature of database contents, along with problems within the field of data mining

Source

Journal of the American Society for Information Science. 49(1998) no.5, S.397-402

0.00849611 = product of:
  0.038232498 = sum of:
    0.016503768 = weight(_text_:of in 601) [ClassicSimilarity], result of:
      0.016503768 = score(doc=601,freq=14.0), product of:
        0.05157766 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03298316 = queryNorm
        0.31997898 = fieldWeight in 601, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0546875 = fieldNorm(doc=601)
    0.02172873 = product of:
      0.04345746 = sum of:
        0.04345746 = weight(_text_:problems in 601) [ClassicSimilarity], result of:
          0.04345746 = score(doc=601,freq=2.0), product of:
            0.13613719 = queryWeight, product of:
              4.1274753 = idf(docFreq=1937, maxDocs=44218)
              0.03298316 = queryNorm
            0.31921813 = fieldWeight in 601, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.1274753 = idf(docFreq=1937, maxDocs=44218)
              0.0546875 = fieldNorm(doc=601)
      0.5 = coord(1/2)
  0.22222222 = coord(2/9)

Abstract

In this paper, we present a framework for clustering Web search engine queries whose aim is to identify groups of queries used to search for similar information on the Web. The framework is based on a novel term vector model of queries that integrates user selections and the content of selected documents extracted from the logs of a search engine. The query representation obtained allows us to treat query clustering similarly to standard document clustering. We study the application of the clustering framework to two problems: relevance ranking boosting and query recommendation. Finally, we evaluate with experiments the effectiveness of our approach.

Source

Journal of the American Society for Information Science and Technology. 58(2007) no.12, S.1793-1804

0.00833467 = product of:
  0.037506014 = sum of:
    0.0106934365 = weight(_text_:of in 6783) [ClassicSimilarity], result of:
      0.0106934365 = score(doc=6783,freq=2.0), product of:
        0.05157766 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03298316 = queryNorm
        0.20732689 = fieldWeight in 6783, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.09375 = fieldNorm(doc=6783)
    0.026812578 = product of:
      0.053625155 = sum of:
        0.053625155 = weight(_text_:22 in 6783) [ClassicSimilarity], result of:
          0.053625155 = score(doc=6783,freq=2.0), product of:
            0.11550141 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03298316 = 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)
  0.22222222 = coord(2/9)

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

0.008262345 = product of:
  0.03718055 = sum of:
    0.012347717 = weight(_text_:of in 3246) [ClassicSimilarity], result of:
      0.012347717 = score(doc=3246,freq=6.0), product of:
        0.05157766 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03298316 = queryNorm
        0.23940048 = fieldWeight in 3246, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0625 = fieldNorm(doc=3246)
    0.024832834 = product of:
      0.049665667 = sum of:
        0.049665667 = weight(_text_:problems in 3246) [ClassicSimilarity], result of:
          0.049665667 = score(doc=3246,freq=2.0), product of:
            0.13613719 = queryWeight, product of:
              4.1274753 = idf(docFreq=1937, maxDocs=44218)
              0.03298316 = queryNorm
            0.36482072 = fieldWeight in 3246, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.1274753 = idf(docFreq=1937, maxDocs=44218)
              0.0625 = fieldNorm(doc=3246)
      0.5 = coord(1/2)
  0.22222222 = coord(2/9)

Abstract

Surveys the use of empirical, machine-learning methods for information extraction. Presents a generic architecture for information extraction systems and surveys the learning algorithms that have been developed to address the problems of accuracy, portability, and knowledge acquisition for each component of the architecture

0.008079485 = product of:
  0.036357682 = sum of:
    0.017733058 = weight(_text_:of in 3704) [ClassicSimilarity], result of:
      0.017733058 = score(doc=3704,freq=22.0), product of:
        0.05157766 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03298316 = queryNorm
        0.34381276 = fieldWeight in 3704, product of:
          4.690416 = tf(freq=22.0), with freq of:
            22.0 = termFreq=22.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=3704)
    0.018624624 = product of:
      0.03724925 = sum of:
        0.03724925 = weight(_text_:problems in 3704) [ClassicSimilarity], result of:
          0.03724925 = score(doc=3704,freq=2.0), product of:
            0.13613719 = queryWeight, product of:
              4.1274753 = idf(docFreq=1937, maxDocs=44218)
              0.03298316 = queryNorm
            0.27361554 = fieldWeight in 3704, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.1274753 = idf(docFreq=1937, maxDocs=44218)
              0.046875 = fieldNorm(doc=3704)
      0.5 = coord(1/2)
  0.22222222 = coord(2/9)

Abstract

Using Google Earth, Google Maps, and/or network visualization programs such as Pajek, one can overlay the network of relations among addresses in scientific publications onto the geographic map. The authors discuss the pros and cons of various options, and provide software (freeware) for bridging existing gaps between the Science Citation Indices (Thomson Reuters) and Scopus (Elsevier), on the one hand, and these various visualization tools on the other. At the level of city names, the global map can be drawn reliably on the basis of the available address information. At the level of the names of organizations and institutes, there are problems of unification both in the ISI databases and with Scopus. Pajek enables a combination of visualization and statistical analysis, whereas the Google Maps and its derivatives provide superior tools on the Internet.

Source

Journal of the American Society for Information Science and Technology. 61(2010) no.8, S.1622-1634

0.007986364 = product of:
  0.035938635 = sum of:
    0.020418115 = weight(_text_:of in 3603) [ClassicSimilarity], result of:
      0.020418115 = score(doc=3603,freq=42.0), product of:
        0.05157766 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03298316 = queryNorm
        0.39587128 = fieldWeight in 3603, product of:
          6.4807405 = tf(freq=42.0), with freq of:
            42.0 = termFreq=42.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3603)
    0.0155205205 = product of:
      0.031041041 = sum of:
        0.031041041 = weight(_text_:problems in 3603) [ClassicSimilarity], result of:
          0.031041041 = score(doc=3603,freq=2.0), product of:
            0.13613719 = queryWeight, product of:
              4.1274753 = idf(docFreq=1937, maxDocs=44218)
              0.03298316 = queryNorm
            0.22801295 = fieldWeight in 3603, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.1274753 = idf(docFreq=1937, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3603)
      0.5 = coord(1/2)
  0.22222222 = coord(2/9)

Abstract

This paper addresses a bibliometric methodology to discover the structure of the scientific 'landscape' in order to gain detailed insight into the development of MD fields, their interaction, and the transfer of knowledge between them. This methodology is appropriate to visualize the position of MD activities in relation to interdisciplinary MD developments, and particularly in relation to socio-economic problems. Furthermore, it allows the identification of the major actors. It even provides the possibility of foresight. We describe a first approach to apply bibliometric mapping as an instrument to investigate characteristics of knowledge transfer. In this paper we discuss the creation of 'maps of science' with help of advanced bibliometric methods. This 'bibliometric cartography' can be seen as a specific type of data-mining, applied to large amounts of scientific publications. As an example we describe the mapping of the field neuroscience, one of the largest and fast growing fields in the life sciences. The number of publications covered by this database is about 80,000 per year, the period covered is 1995-1998. Current research is going an to update the mapping for the years 1999-2002. This paper addresses the main lines of the methodology and its application in the study of knowledge transfer.

Source

Gaining insight from research information (CRIS2002): Proceedings of the 6th International Conference an Current Research Information Systems, University of Kassel, August 29 - 31, 2002. Eds: W. Adamczak u. A. Nase

0.0074994285 = product of:
  0.033747427 = sum of:
    0.015122802 = weight(_text_:of in 3144) [ClassicSimilarity], result of:
      0.015122802 = score(doc=3144,freq=16.0), product of:
        0.05157766 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03298316 = queryNorm
        0.2932045 = fieldWeight in 3144, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=3144)
    0.018624624 = product of:
      0.03724925 = sum of:
        0.03724925 = weight(_text_:problems in 3144) [ClassicSimilarity], result of:
          0.03724925 = score(doc=3144,freq=2.0), product of:
            0.13613719 = queryWeight, product of:
              4.1274753 = idf(docFreq=1937, maxDocs=44218)
              0.03298316 = queryNorm
            0.27361554 = fieldWeight in 3144, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.1274753 = idf(docFreq=1937, maxDocs=44218)
              0.046875 = fieldNorm(doc=3144)
      0.5 = coord(1/2)
  0.22222222 = coord(2/9)

Abstract

Data Mining describes a technology that discovers non-trivial hidden patterns in a large collection of data. Although this technology has a tremendous impact on our lives, the invaluable contributions of this invisible technology often go unnoticed. This paper discusses advances in data mining while focusing on the emerging data mining capability. Such data mining applications perform multidimensional mining on a wide variety of heterogeneous data sources, providing solutions to many unresolved problems. This paper also highlights the advantages and disadvantages arising from the ever-expanding scope of data mining. Data Mining augments human intelligence by equipping us with a wealth of knowledge and by empowering us to perform our daily tasks better. As the mining scope and capacity increases, users and organizations become more willing to compromise privacy. The huge data stores of the 'master miners' allow them to gain deep insights into individual lifestyles and their social and behavioural patterns. Data integration and analysis capability of combining business and financial trends together with the ability to deterministically track market changes will drastically affect our lives.

0.0071432088 = product of:
  0.03214444 = sum of:
    0.016503768 = weight(_text_:of in 2908) [ClassicSimilarity], result of:
      0.016503768 = score(doc=2908,freq=14.0), product of:
        0.05157766 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03298316 = queryNorm
        0.31997898 = fieldWeight in 2908, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0546875 = fieldNorm(doc=2908)
    0.01564067 = product of:
      0.03128134 = sum of:
        0.03128134 = weight(_text_:22 in 2908) [ClassicSimilarity], result of:
          0.03128134 = score(doc=2908,freq=2.0), product of:
            0.11550141 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03298316 = queryNorm
            0.2708308 = fieldWeight in 2908, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0546875 = fieldNorm(doc=2908)
      0.5 = coord(1/2)
  0.22222222 = coord(2/9)

Abstract

Focuses on the information modelling side of conceptual modelling. Deals with the exploitation of fact verbalisations after finishing the actual information system. Verbalisations are used as input for the design of the so-called information model. Exploits these verbalisation in 4 directions: considers their use for a conceptual query language, the verbalisation of instances, the description of the contents of a database and for the verbalisation of queries in a computer supported query environment. Provides an example session with an envisioned tool for end user query formulations that exploits the verbalisation

Source

Information systems. 22(1997) nos.5/6, S.349-385