Search (45 results, page 1 of 3)

0.027005369 = product of:
  0.12602505 = sum of:
    0.059409913 = weight(_text_:subject in 5481) [ClassicSimilarity], result of:
      0.059409913 = score(doc=5481,freq=8.0), product of:
        0.10738805 = queryWeight, product of:
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.03002521 = queryNorm
        0.5532265 = fieldWeight in 5481, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.0546875 = fieldNorm(doc=5481)
    0.033307575 = weight(_text_:classification in 5481) [ClassicSimilarity], result of:
      0.033307575 = score(doc=5481,freq=4.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.34832728 = fieldWeight in 5481, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.0546875 = fieldNorm(doc=5481)
    0.033307575 = weight(_text_:classification in 5481) [ClassicSimilarity], result of:
      0.033307575 = score(doc=5481,freq=4.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.34832728 = fieldWeight in 5481, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.0546875 = fieldNorm(doc=5481)
  0.21428572 = coord(3/14)

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.

Source

Cataloging and classification quarterly. 57(2019) no.5, S.315-336

0.02084957 = product of:
  0.097298 = sum of:
    0.030006537 = weight(_text_:subject in 5653) [ClassicSimilarity], result of:
      0.030006537 = score(doc=5653,freq=4.0), product of:
        0.10738805 = queryWeight, product of:
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.03002521 = queryNorm
        0.27942157 = fieldWeight in 5653, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5653)
    0.03364573 = weight(_text_:classification in 5653) [ClassicSimilarity], result of:
      0.03364573 = score(doc=5653,freq=8.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.35186368 = fieldWeight in 5653, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5653)
    0.03364573 = weight(_text_:classification in 5653) [ClassicSimilarity], result of:
      0.03364573 = score(doc=5653,freq=8.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.35186368 = fieldWeight in 5653, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5653)
  0.21428572 = coord(3/14)

Abstract

Discusses the importance of knowledge organization in the context of the information overload caused by the vast quantities of data and information accessible on internal and external networks of an organization. Defines the characteristics of a knowledge-based product. Elaborates on the techniques and applications of text mining in developing knowledge products. Presents two approaches, as case studies, to the making of knowledge products: (1) steps and processes in the planning, designing and development of a composite multilingual multimedia CD product, with the potential international, inter-cultural end users in view, and (2) application of natural language processing software in text mining. Using a text mining software, it is possible to link concept terms from a processed text to a related thesaurus, glossary, schedules of a classification scheme, and facet structured subject representations. Concludes that the products of text mining and data mining could be made more useful if the features of a faceted scheme for subject classification are incorporated into text mining techniques and products.

Content

Beitrag eines Themenheftes "Knowledge organization and classification in international information retrieval"

Source

Cataloging and classification quarterly. 37(2003) nos.1/2, S.96-114

0.020441301 = product of:
  0.095392734 = sum of:
    0.02546139 = weight(_text_:subject in 720) [ClassicSimilarity], result of:
      0.02546139 = score(doc=720,freq=2.0), product of:
        0.10738805 = queryWeight, product of:
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.03002521 = queryNorm
        0.23709705 = fieldWeight in 720, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.046875 = fieldNorm(doc=720)
    0.03496567 = weight(_text_:classification in 720) [ClassicSimilarity], result of:
      0.03496567 = score(doc=720,freq=6.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.3656675 = fieldWeight in 720, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.046875 = fieldNorm(doc=720)
    0.03496567 = weight(_text_:classification in 720) [ClassicSimilarity], result of:
      0.03496567 = score(doc=720,freq=6.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.3656675 = fieldWeight in 720, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.046875 = fieldNorm(doc=720)
  0.21428572 = coord(3/14)

Abstract

This project brought together undergraduate students in Computer Science with librarians to mine abstracts of articles from the Texas A&M University Libraries' institutional repository, OAKTrust, in order to probe the creation of new metadata to improve discovery and use. The mining operation task consisted simply of classifying the articles into two categories of research type: basic research ("for understanding," "curiosity-based," or "knowledge-based") and applied research ("use-based"). These categories are fundamental especially for funders but are also important to researchers. The mining-to-classification steps took several iterations, but ultimately, we achieved good results with the toolkit BERT (Bidirectional Encoder Representations from Transformers). The project and its workflows represent a preview of what may lie ahead in the future of crafting metadata using text mining techniques to enhance discoverability.

Footnote

Teil eines Themenheftes: Artificial intelligence (AI) and automated processes for subject sccess

Source

Cataloging and classification quarterly. 59(2021) no.8, p.815-834

0.018966276 = product of:
  0.08850929 = sum of:
    0.021217827 = weight(_text_:subject in 5997) [ClassicSimilarity], result of:
      0.021217827 = score(doc=5997,freq=2.0), product of:
        0.10738805 = queryWeight, product of:
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.03002521 = queryNorm
        0.19758089 = fieldWeight in 5997, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5997)
    0.03364573 = weight(_text_:classification in 5997) [ClassicSimilarity], result of:
      0.03364573 = score(doc=5997,freq=8.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.35186368 = fieldWeight in 5997, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5997)
    0.03364573 = weight(_text_:classification in 5997) [ClassicSimilarity], result of:
      0.03364573 = score(doc=5997,freq=8.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.35186368 = fieldWeight in 5997, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5997)
  0.21428572 = coord(3/14)

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.

Series

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

0.015061316 = product of:
  0.07028614 = sum of:
    0.02018744 = weight(_text_:classification in 3015) [ClassicSimilarity], result of:
      0.02018744 = score(doc=3015,freq=2.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.21111822 = fieldWeight in 3015, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.046875 = fieldNorm(doc=3015)
    0.02018744 = weight(_text_:classification in 3015) [ClassicSimilarity], result of:
      0.02018744 = score(doc=3015,freq=2.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.21111822 = fieldWeight in 3015, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.046875 = fieldNorm(doc=3015)
    0.029911257 = product of:
      0.059822515 = sum of:
        0.059822515 = weight(_text_:texts in 3015) [ClassicSimilarity], result of:
          0.059822515 = score(doc=3015,freq=2.0), product of:
            0.16460659 = queryWeight, product of:
              5.4822793 = idf(docFreq=499, maxDocs=44218)
              0.03002521 = queryNorm
            0.36342722 = fieldWeight in 3015, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.4822793 = idf(docFreq=499, maxDocs=44218)
              0.046875 = fieldNorm(doc=3015)
      0.5 = coord(1/2)
  0.21428572 = coord(3/14)

Abstract

We analyze the linguistic evolution of selected scientific disciplines over a 30-year time span (1970s to 2000s). Our focus is on four highly specialized disciplines at the boundaries of computer science that emerged during that time: computational linguistics, bioinformatics, digital construction, and microelectronics. Our analysis is driven by the question whether these disciplines develop a distinctive language use-both individually and collectively-over the given time period. The data set is the English Scientific Text Corpus (scitex), which includes texts from the 1970s/1980s and early 2000s. Our theoretical basis is register theory. In terms of methods, we combine corpus-based methods of feature extraction (various aggregated features [part-of-speech based], n-grams, lexico-grammatical patterns) and automatic text classification. The results of our research are directly relevant to the study of linguistic variation and languages for specific purposes (LSP) and have implications for various natural language processing (NLP) tasks, for example, authorship attribution, text mining, or training NLP tools.

0.011864894 = product of:
  0.055369504 = sum of:
    0.00951645 = weight(_text_:classification in 1789) [ClassicSimilarity], result of:
      0.00951645 = score(doc=1789,freq=4.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.099522084 = fieldWeight in 1789, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.015625 = fieldNorm(doc=1789)
    0.00951645 = weight(_text_:classification in 1789) [ClassicSimilarity], result of:
      0.00951645 = score(doc=1789,freq=4.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.099522084 = fieldWeight in 1789, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.015625 = fieldNorm(doc=1789)
    0.036336605 = sum of:
      0.028200602 = weight(_text_:texts in 1789) [ClassicSimilarity], result of:
        0.028200602 = score(doc=1789,freq=4.0), product of:
          0.16460659 = queryWeight, product of:
            5.4822793 = idf(docFreq=499, maxDocs=44218)
            0.03002521 = queryNorm
          0.17132123 = fieldWeight in 1789, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            5.4822793 = idf(docFreq=499, maxDocs=44218)
            0.015625 = fieldNorm(doc=1789)
      0.008136002 = weight(_text_:22 in 1789) [ClassicSimilarity], result of:
        0.008136002 = score(doc=1789,freq=2.0), product of:
          0.10514317 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.03002521 = 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)
  0.21428572 = coord(3/14)

Date

23. 3.2008 19:10:22

Footnote

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."

0.010875943 = product of:
  0.0761316 = sum of:
    0.0380658 = weight(_text_:classification in 505) [ClassicSimilarity], result of:
      0.0380658 = score(doc=505,freq=4.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.39808834 = fieldWeight in 505, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.0625 = fieldNorm(doc=505)
    0.0380658 = weight(_text_:classification in 505) [ClassicSimilarity], result of:
      0.0380658 = score(doc=505,freq=4.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.39808834 = fieldWeight in 505, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.0625 = fieldNorm(doc=505)
  0.14285715 = coord(2/14)

Abstract

The behavior of modern web robots varies widely when they crawl for different purposes. In this article, we present a framework to classify these web robots from two orthogonal perspectives, namely, their functionality and the types of resources they consume. Applying the classification framework to a year-long access log from the UConn SoE web server, we present trends that point to significant differences in their crawling behavior.

0.0100773005 = product of:
  0.0470274 = sum of:
    0.013458292 = weight(_text_:classification in 2222) [ClassicSimilarity], result of:
      0.013458292 = score(doc=2222,freq=2.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.14074548 = fieldWeight in 2222, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03125 = fieldNorm(doc=2222)
    0.020110816 = weight(_text_:bibliographic in 2222) [ClassicSimilarity], result of:
      0.020110816 = score(doc=2222,freq=2.0), product of:
        0.11688946 = queryWeight, product of:
          3.893044 = idf(docFreq=2449, maxDocs=44218)
          0.03002521 = queryNorm
        0.17204987 = fieldWeight in 2222, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.893044 = idf(docFreq=2449, maxDocs=44218)
          0.03125 = fieldNorm(doc=2222)
    0.013458292 = weight(_text_:classification in 2222) [ClassicSimilarity], result of:
      0.013458292 = score(doc=2222,freq=2.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.14074548 = fieldWeight in 2222, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03125 = fieldNorm(doc=2222)
  0.21428572 = coord(3/14)

Footnote

Rez. in: JASIST 55(2004) no.3, S.275-276 (C. Chen): "This is a book about finding significant statistical patterns on the Web - in particular, patterns that are associated with hypertext documents, topics, hyperlinks, and queries. The term pattern in this book refers to dependencies among such items. On the one hand, the Web contains useful information an just about every topic under the sun. On the other hand, just like searching for a needle in a haystack, one would need powerful tools to locate useful information an the vast land of the Web. Soumen Chakrabarti's book focuses an a wide range of techniques for machine learning and data mining an the Web. The goal of the book is to provide both the technical Background and tools and tricks of the trade of Web content mining. Much of the technical content reflects the state of the art between 1995 and 2002. The targeted audience is researchers and innovative developers in this area, as well as newcomers who intend to enter this area. The book begins with an introduction chapter. The introduction chapter explains fundamental concepts such as crawling and indexing as well as clustering and classification. The remaining eight chapters are organized into three parts: i) infrastructure, ii) learning and iii) applications.
Part I, Infrastructure, has two chapters: Chapter 2 on crawling the Web and Chapter 3 an Web search and information retrieval. The second part of the book, containing chapters 4, 5, and 6, is the centerpiece. This part specifically focuses an machine learning in the context of hypertext. Part III is a collection of applications that utilize the techniques described in earlier chapters. Chapter 7 is an social network analysis. Chapter 8 is an resource discovery. Chapter 9 is an the future of Web mining. Overall, this is a valuable reference book for researchers and developers in the field of Web mining. It should be particularly useful for those who would like to design and probably code their own Computer programs out of the equations and pseudocodes an most of the pages. For a student, the most valuable feature of the book is perhaps the formal and consistent treatments of concepts across the board. For what is behind and beyond the technical details, one has to either dig deeper into the bibliographic notes at the end of each chapter, or resort to more in-depth analysis of relevant subjects in the literature. lf you are looking for successful stories about Web mining or hard-way-learned lessons of failures, this is not the book."

0.009516451 = product of:
  0.06661515 = sum of:
    0.033307575 = weight(_text_:classification in 2909) [ClassicSimilarity], result of:
      0.033307575 = score(doc=2909,freq=4.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.34832728 = fieldWeight in 2909, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.0546875 = fieldNorm(doc=2909)
    0.033307575 = weight(_text_:classification in 2909) [ClassicSimilarity], result of:
      0.033307575 = score(doc=2909,freq=4.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.34832728 = fieldWeight in 2909, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.0546875 = fieldNorm(doc=2909)
  0.14285715 = coord(2/14)

Abstract

Rough set theory is a mathematical tool to deal with vagueness and uncertainty. To apply the theory, it needs to be associated with efficient and effective computational methods. A relation can be used to represent a decison table for use in decision making. By using this kind of table, rough set theory can be applied successfully to rough classification and knowledge discovery. Presents computational methods for using rough sets to identify classes in datasets, finding dependencies in relations, and discovering rules which are hidden in databases. Illustrates the methods with a running example from a database of car test results

0.00832516 = product of:
  0.058276117 = sum of:
    0.029138058 = weight(_text_:classification in 3607) [ClassicSimilarity], result of:
      0.029138058 = score(doc=3607,freq=6.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.3047229 = fieldWeight in 3607, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3607)
    0.029138058 = weight(_text_:classification in 3607) [ClassicSimilarity], result of:
      0.029138058 = score(doc=3607,freq=6.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.3047229 = fieldWeight in 3607, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3607)
  0.14285715 = coord(2/14)

Abstract

The abundance of medical resources has encouraged the development of systems that allow for efficient searches of information in large medical image data sets. State-of-the-art image retrieval models are classified into three categories: content-based (visual) models, textual models, and combined models. Content-based models use visual features to answer image queries, textual image retrieval models use word matching to answer textual queries, and combined image retrieval models, use both textual and visual features to answer queries. Nevertheless, most of previous works in this field have used the same image retrieval model independently of the query type. In this article, we define a list of generic and specific medical query features and exploit them in an association rule mining technique to discover correlations between query features and image retrieval models. Based on these rules, we propose to use an associative classifier (NaiveClass) to find the best suitable retrieval model given a new textual query. We also propose a second associative classifier (SmartClass) to select the most appropriate default class for the query. Experiments are performed on Medical ImageCLEF queries from 2008 to 2012 to evaluate the impact of the proposed query features on the classification performance. The results show that combining our proposed specific and generic query features is effective in query classification.

0.008156957 = product of:
  0.057098698 = sum of:
    0.028549349 = weight(_text_:classification in 2911) [ClassicSimilarity], result of:
      0.028549349 = score(doc=2911,freq=4.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.29856625 = fieldWeight in 2911, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.046875 = fieldNorm(doc=2911)
    0.028549349 = weight(_text_:classification in 2911) [ClassicSimilarity], result of:
      0.028549349 = score(doc=2911,freq=4.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.29856625 = fieldWeight in 2911, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.046875 = fieldNorm(doc=2911)
  0.14285715 = coord(2/14)

Abstract

Develops and analyzes 4 algorithms for feature selection in the context of rough set methodology. Develops the notion of accuracy of classification that can be used for upper or lower classification methods and defines the feature selection problem. Presents a discussion of upper classifiers and develops 4 features selection heuristics and discusses the family of stepwise backward selection algorithms. Analyzes the worst case time complexity in all algorithms presented. Discusses details of the experiments and results of using a family of stepwise backward selection learning data sets and a duodenal ulcer data set. Includes the experimental setup and results of comparison of lower classifiers and upper classiers on the duodenal ulcer data set. Discusses exteded decision tables

0.008156957 = product of:
  0.057098698 = sum of:
    0.028549349 = weight(_text_:classification in 92) [ClassicSimilarity], result of:
      0.028549349 = score(doc=92,freq=4.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.29856625 = fieldWeight in 92, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.046875 = fieldNorm(doc=92)
    0.028549349 = weight(_text_:classification in 92) [ClassicSimilarity], result of:
      0.028549349 = score(doc=92,freq=4.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.29856625 = fieldWeight in 92, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.046875 = fieldNorm(doc=92)
  0.14285715 = coord(2/14)

Abstract

In this paper the authors will present research on the combination of two methods of data mining: text classification and maximal association rules. Text classification has been the focus of interest of many researchers for a long time. However, the results take the form of lists of words (classes) that people often do not know what to do with. The use of maximal association rules induced a number of advantages: (1) the detection of dependencies and correlations between the relevant units of information (words) of different classes, (2) the extraction of hidden knowledge, often relevant, from a large volume of data. The authors will show how this combination can improve the process of information retrieval.

0.007690453 = product of:
  0.053833168 = sum of:
    0.026916584 = weight(_text_:classification in 6625) [ClassicSimilarity], result of:
      0.026916584 = score(doc=6625,freq=2.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.28149095 = fieldWeight in 6625, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.0625 = fieldNorm(doc=6625)
    0.026916584 = weight(_text_:classification in 6625) [ClassicSimilarity], result of:
      0.026916584 = score(doc=6625,freq=2.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.28149095 = fieldWeight in 6625, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.0625 = fieldNorm(doc=6625)
  0.14285715 = coord(2/14)

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

0.0067974646 = product of:
  0.04758225 = sum of:
    0.023791125 = weight(_text_:classification in 1867) [ClassicSimilarity], result of:
      0.023791125 = score(doc=1867,freq=4.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.24880521 = fieldWeight in 1867, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1867)
    0.023791125 = weight(_text_:classification in 1867) [ClassicSimilarity], result of:
      0.023791125 = score(doc=1867,freq=4.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.24880521 = fieldWeight in 1867, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1867)
  0.14285715 = coord(2/14)

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.

0.0067974646 = product of:
  0.04758225 = sum of:
    0.023791125 = weight(_text_:classification in 967) [ClassicSimilarity], result of:
      0.023791125 = score(doc=967,freq=4.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.24880521 = fieldWeight in 967, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.0390625 = fieldNorm(doc=967)
    0.023791125 = weight(_text_:classification in 967) [ClassicSimilarity], result of:
      0.023791125 = score(doc=967,freq=4.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.24880521 = fieldWeight in 967, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.0390625 = fieldNorm(doc=967)
  0.14285715 = coord(2/14)

Abstract

Because of Twitter's popularity and the viral nature of information dissemination on Twitter, predicting which Twitter topics will become popular in the near future becomes a task of considerable economic importance. Many Twitter topics are annotated by hashtags. In this article, we propose methods to predict the popularity of new hashtags on Twitter by formulating the problem as a classification task. We use five standard classification models (i.e., Naïve bayes, k-nearest neighbors, decision trees, support vector machines, and logistic regression) for prediction. The main challenge is the identification of effective features for describing new hashtags. We extract 7 content features from a hashtag string and the collection of tweets containing the hashtag and 11 contextual features from the social graph formed by users who have adopted the hashtag. We conducted experiments on a Twitter data set consisting of 31 million tweets from 2 million Singapore-based users. The experimental results show that the standard classifiers using the extracted features significantly outperform the baseline methods that do not use these features. Among the five classifiers, the logistic regression model performs the best in terms of the Micro-F1 measure. We also observe that contextual features are more effective than content features.

0.0067291465 = product of:
  0.047104023 = sum of:
    0.023552012 = weight(_text_:classification in 3821) [ClassicSimilarity], result of:
      0.023552012 = score(doc=3821,freq=2.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.24630459 = fieldWeight in 3821, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.0546875 = fieldNorm(doc=3821)
    0.023552012 = weight(_text_:classification in 3821) [ClassicSimilarity], result of:
      0.023552012 = score(doc=3821,freq=2.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.24630459 = fieldWeight in 3821, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.0546875 = fieldNorm(doc=3821)
  0.14285715 = coord(2/14)

Abstract

The 29 revised full papers presented together with 37 short papers were carefully selected from a total of 158 submissions. The book is divided into sections on emerging KDD technology; association rules; feature selection and generation; mining in semi-unstructured data; interestingness, surprisingness, and exceptions; rough sets, fuzzy logic, and neural networks; induction, classification, and clustering; visualization, causal models and graph-based methods; agent-based and distributed data mining; and advanced topics and new methodologies

0.00576784 = product of:
  0.04037488 = sum of:
    0.02018744 = weight(_text_:classification in 4296) [ClassicSimilarity], result of:
      0.02018744 = score(doc=4296,freq=2.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.21111822 = fieldWeight in 4296, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.046875 = fieldNorm(doc=4296)
    0.02018744 = weight(_text_:classification in 4296) [ClassicSimilarity], result of:
      0.02018744 = score(doc=4296,freq=2.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.21111822 = fieldWeight in 4296, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.046875 = fieldNorm(doc=4296)
  0.14285715 = coord(2/14)

Abstract

Data mining (DM) is a multistaged process of extracting previously unanticipated knowledge from large databases, and applying the results to decision making. Data mining tools detect patterns from the data and infer associations and rules from them. The extracted information may then be applied to prediction or classification models by identifying relations within the data records or between databases. Those patterns and rules can then guide decision making and forecast the effects of those decisions. However, this definition may be applied equally to "knowledge discovery in databases" (KDD). Indeed, in the recent literature of DM and KDD, a source of confusion has emerged, making it difficult to determine the exact parameters of both. KDD is sometimes viewed as the broader discipline, of which data mining is merely a component-specifically pattern extraction, evaluation, and cleansing methods (Raghavan, Deogun, & Sever, 1998, p. 397). Thurasingham (1999, p. 2) remarked that "knowledge discovery," "pattern discovery," "data dredging," "information extraction," and "knowledge mining" are all employed as synonyms for DM. Trybula, in his ARIST chapter an text mining, observed that the "existing work [in KDD] is confusing because the terminology is inconsistent and poorly defined.

0.00576784 = product of:
  0.04037488 = sum of:
    0.02018744 = weight(_text_:classification in 916) [ClassicSimilarity], result of:
      0.02018744 = score(doc=916,freq=2.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.21111822 = fieldWeight in 916, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.046875 = fieldNorm(doc=916)
    0.02018744 = weight(_text_:classification in 916) [ClassicSimilarity], result of:
      0.02018744 = score(doc=916,freq=2.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.21111822 = fieldWeight in 916, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.046875 = fieldNorm(doc=916)
  0.14285715 = coord(2/14)

Abstract

In this paper, we present a topic discovery system aimed to reveal the implicit knowledge present in news streams. This knowledge is expressed as a hierarchy of topic/subtopics, where each topic contains the set of documents that are related to it and a summary extracted from these documents. Summaries so built are useful to browse and select topics of interest from the generated hierarchies. Our proposal consists of a new incremental hierarchical clustering algorithm, which combines both partitional and agglomerative approaches, taking the main benefits from them. Finally, a new summarization method based on Testor Theory has been proposed to build the topic summaries. Experimental results in the TDT2 collection demonstrate its usefulness and effectiveness not only as a topic detection system, but also as a classification and summarization tool.

0.00576784 = product of:
  0.04037488 = sum of:
    0.02018744 = weight(_text_:classification in 2400) [ClassicSimilarity], result of:
      0.02018744 = score(doc=2400,freq=2.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.21111822 = fieldWeight in 2400, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.046875 = fieldNorm(doc=2400)
    0.02018744 = weight(_text_:classification in 2400) [ClassicSimilarity], result of:
      0.02018744 = score(doc=2400,freq=2.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.21111822 = fieldWeight in 2400, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.046875 = fieldNorm(doc=2400)
  0.14285715 = coord(2/14)

Abstract

We propose a text classification tool based on support vector machines for the assessment of organizational leadership styles, as appearing to Twitter users. We collected Twitter data over 51 days, related to the first 30 Italian organizations in the 2015 ranking of Forbes Global 2000-out of which we selected the five with the most relevant volumes of tweets. We analyzed the communication of the company leaders, together with the dialogue among the stakeholders of each company, to understand the association with perceived leadership styles and dimensions. To assess leadership profiles, we referred to the 10-factor model developed by Barchiesi and La Bella in 2007. We maintain the distinctiveness of the approach we propose, as it allows a rapid assessment of the perceived leadership capabilities of an enterprise, as they emerge from its social media interactions. It can also be used to show how companies respond and manage their communication when specific events take place, and to assess their stakeholder's reactions.

0.004806533 = product of:
  0.03364573 = sum of:
    0.016822865 = weight(_text_:classification in 804) [ClassicSimilarity], result of:
      0.016822865 = score(doc=804,freq=2.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.17593184 = fieldWeight in 804, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.0390625 = fieldNorm(doc=804)
    0.016822865 = weight(_text_:classification in 804) [ClassicSimilarity], result of:
      0.016822865 = score(doc=804,freq=2.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.17593184 = fieldWeight in 804, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.0390625 = fieldNorm(doc=804)
  0.14285715 = coord(2/14)