Search (63 results, page 1 of 4)

0.048943583 = product of:
  0.097887166 = sum of:
    0.097887166 = sum of:
      0.04078141 = weight(_text_:m in 4261) [ClassicSimilarity], result of:
        0.04078141 = score(doc=4261,freq=4.0), product of:
          0.13110629 = queryWeight, product of:
            2.4884486 = idf(docFreq=9980, maxDocs=44218)
            0.052685954 = queryNorm
          0.31105608 = fieldWeight in 4261, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            2.4884486 = idf(docFreq=9980, maxDocs=44218)
            0.0625 = fieldNorm(doc=4261)
      0.057105757 = weight(_text_:22 in 4261) [ClassicSimilarity], result of:
        0.057105757 = score(doc=4261,freq=2.0), product of:
          0.18449724 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052685954 = queryNorm
          0.30952093 = fieldWeight in 4261, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0625 = fieldNorm(doc=4261)
  0.5 = coord(1/2)

Date

17. 7.2002 19:22:06

Type

m

0.0329677 = sum of:
  0.015122154 = product of:
    0.060488615 = sum of:
      0.060488615 = weight(_text_:authors in 668) [ClassicSimilarity], result of:
        0.060488615 = score(doc=668,freq=2.0), product of:
          0.24018547 = queryWeight, product of:
            4.558814 = idf(docFreq=1258, maxDocs=44218)
            0.052685954 = queryNorm
          0.25184128 = fieldWeight in 668, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            4.558814 = idf(docFreq=1258, maxDocs=44218)
            0.0390625 = fieldNorm(doc=668)
    0.25 = coord(1/4)
  0.017845549 = product of:
    0.035691097 = sum of:
      0.035691097 = weight(_text_:22 in 668) [ClassicSimilarity], result of:
        0.035691097 = score(doc=668,freq=2.0), product of:
          0.18449724 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052685954 = queryNorm
          0.19345059 = fieldWeight in 668, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0390625 = fieldNorm(doc=668)
    0.5 = coord(1/2)

Abstract

20th century massification of higher education and research in academia is said to have produced structurally stratified higher education systems in many countries. Most manifestly, the research mission of universities appears to be divisive. Authors have claimed that the Swedish system, while formally unified, has developed into a binary state, and statistics seem to support this conclusion. This article makes use of a comprehensive statistical data source on Swedish higher education institutions to illustrate stratification, and uses literature on Swedish research policy history to contextualize the statistics. Highlighting the opportunities as well as constraints of the data, the article argues that there is great merit in combining statistics with a qualitative analysis when studying the structural characteristics of national higher education systems. Not least the article shows that it is an over-simplification to describe the Swedish system as binary; the stratification is more complex. On basis of the analysis, the article also argues that while global trends certainly influence national developments, higher education systems have country-specific features that may enrich the understanding of how systems evolve and therefore should be analyzed as part of a broader study of the increasingly globalized academic system.

Date

22. 3.2013 19:43:01

0.032228462 = product of:
  0.064456925 = sum of:
    0.064456925 = sum of:
      0.021627607 = weight(_text_:m in 1383) [ClassicSimilarity], result of:
        0.021627607 = score(doc=1383,freq=2.0), product of:
          0.13110629 = queryWeight, product of:
            2.4884486 = idf(docFreq=9980, maxDocs=44218)
            0.052685954 = queryNorm
          0.1649624 = fieldWeight in 1383, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            2.4884486 = idf(docFreq=9980, maxDocs=44218)
            0.046875 = fieldNorm(doc=1383)
      0.042829316 = weight(_text_:22 in 1383) [ClassicSimilarity], result of:
        0.042829316 = score(doc=1383,freq=2.0), product of:
          0.18449724 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052685954 = queryNorm
          0.23214069 = fieldWeight in 1383, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046875 = fieldNorm(doc=1383)
  0.5 = coord(1/2)

Date

22. 3.2008 14:46:06

Type

m

0.030040024 = sum of:
  0.008554382 = product of:
    0.03421753 = sum of:
      0.03421753 = weight(_text_:authors in 1789) [ClassicSimilarity], result of:
        0.03421753 = score(doc=1789,freq=4.0), product of:
          0.24018547 = queryWeight, product of:
            4.558814 = idf(docFreq=1258, maxDocs=44218)
            0.052685954 = queryNorm
          0.14246294 = fieldWeight in 1789, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            4.558814 = idf(docFreq=1258, maxDocs=44218)
            0.015625 = fieldNorm(doc=1789)
    0.25 = coord(1/4)
  0.021485642 = sum of:
    0.0072092023 = weight(_text_:m in 1789) [ClassicSimilarity], result of:
      0.0072092023 = score(doc=1789,freq=2.0), product of:
        0.13110629 = queryWeight, product of:
          2.4884486 = idf(docFreq=9980, maxDocs=44218)
          0.052685954 = queryNorm
        0.054987464 = fieldWeight in 1789, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.4884486 = idf(docFreq=9980, maxDocs=44218)
          0.015625 = fieldNorm(doc=1789)
    0.014276439 = weight(_text_:22 in 1789) [ClassicSimilarity], result of:
      0.014276439 = score(doc=1789,freq=2.0), product of:
        0.18449724 = queryWeight, product of:
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.052685954 = 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)

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."

Type

m

0.028960388 = sum of:
  0.018146584 = product of:
    0.072586335 = sum of:
      0.072586335 = weight(_text_:authors in 4242) [ClassicSimilarity], result of:
        0.072586335 = score(doc=4242,freq=2.0), product of:
          0.24018547 = queryWeight, product of:
            4.558814 = idf(docFreq=1258, maxDocs=44218)
            0.052685954 = queryNorm
          0.30220953 = fieldWeight in 4242, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            4.558814 = idf(docFreq=1258, maxDocs=44218)
            0.046875 = fieldNorm(doc=4242)
    0.25 = coord(1/4)
  0.010813803 = product of:
    0.021627607 = sum of:
      0.021627607 = weight(_text_:m in 4242) [ClassicSimilarity], result of:
        0.021627607 = score(doc=4242,freq=2.0), product of:
          0.13110629 = queryWeight, product of:
            2.4884486 = idf(docFreq=9980, maxDocs=44218)
            0.052685954 = queryNorm
          0.1649624 = fieldWeight in 4242, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            2.4884486 = idf(docFreq=9980, maxDocs=44218)
            0.046875 = fieldNorm(doc=4242)
    0.5 = coord(1/2)

Abstract

With more than two billion pages created by millions of Web page authors and organizations, the World Wide Web is a tremendously rich knowledge base. The knowledge comes not only from the content of the pages themselves, but also from the unique characteristics of the Web, such as its hyperlink structure and its diversity of content and languages. Analysis of these characteristics often reveals interesting patterns and new knowledge. Such knowledge can be used to improve users' efficiency and effectiveness in searching for information an the Web, and also for applications unrelated to the Web, such as support for decision making or business management. The Web's size and its unstructured and dynamic content, as well as its multilingual nature, make the extraction of useful knowledge a challenging research problem. Furthermore, the Web generates a large amount of data in other formats that contain valuable information. For example, Web server logs' information about user access patterns can be used for information personalization or improving Web page design.

0.026857052 = product of:
  0.053714104 = sum of:
    0.053714104 = sum of:
      0.018023007 = weight(_text_:m in 5011) [ClassicSimilarity], result of:
        0.018023007 = score(doc=5011,freq=2.0), product of:
          0.13110629 = queryWeight, product of:
            2.4884486 = idf(docFreq=9980, maxDocs=44218)
            0.052685954 = queryNorm
          0.13746867 = fieldWeight in 5011, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            2.4884486 = idf(docFreq=9980, maxDocs=44218)
            0.0390625 = fieldNorm(doc=5011)
      0.035691097 = weight(_text_:22 in 5011) [ClassicSimilarity], result of:
        0.035691097 = score(doc=5011,freq=2.0), product of:
          0.18449724 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052685954 = queryNorm
          0.19345059 = fieldWeight in 5011, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0390625 = fieldNorm(doc=5011)
  0.5 = coord(1/2)

Date

7. 3.2019 16:32:22

0.024983767 = product of:
  0.049967535 = sum of:
    0.049967535 = product of:
      0.09993507 = sum of:
        0.09993507 = weight(_text_:22 in 4577) [ClassicSimilarity], result of:
          0.09993507 = score(doc=4577,freq=2.0), product of:
            0.18449724 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052685954 = queryNorm
            0.5416616 = fieldWeight in 4577, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.109375 = fieldNorm(doc=4577)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

2. 4.2000 18:01:22

0.021485642 = product of:
  0.042971283 = sum of:
    0.042971283 = sum of:
      0.014418405 = weight(_text_:m in 2403) [ClassicSimilarity], result of:
        0.014418405 = score(doc=2403,freq=2.0), product of:
          0.13110629 = queryWeight, product of:
            2.4884486 = idf(docFreq=9980, maxDocs=44218)
            0.052685954 = queryNorm
          0.10997493 = fieldWeight in 2403, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            2.4884486 = idf(docFreq=9980, maxDocs=44218)
            0.03125 = fieldNorm(doc=2403)
      0.028552879 = weight(_text_:22 in 2403) [ClassicSimilarity], result of:
        0.028552879 = score(doc=2403,freq=2.0), product of:
          0.18449724 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052685954 = queryNorm
          0.15476047 = fieldWeight in 2403, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.03125 = fieldNorm(doc=2403)
  0.5 = coord(1/2)

Content

"Vor Google gibt es kein Entrinnen. Nun macht sich die größte Internetsuchmaschine daran, auch gefährliche Grippewellen in den USA vorauszusagen - und das schneller als die US-Gesundheitsbehörde. In den Regionen, in denen die Influenza grassiert, häufen sich erfahrungsgemäß auch die Online-Anfragen im Internet speziell zu diesem Thema. "Wir haben einen engen Zusammenhang feststellen können zwischen Personen, die nach themenbezogenen Informationen suchen, und Personen, die tatsächlich an der Grippe erkrankt sind", schreibt Google. Ein Webtool namens "Google Flu Trends" errechnet aus den Anfragen die Ausbreitung von Grippeviren. Auch wenn nicht jeder Nutzer erkrankt sei, spiegele die Zahl der Anfragen doch ziemlich genau die Entwicklung einer Grippewelle wider. Das belege ein Vergleich mit den Daten der US-Seuchenkontrollbehörde CDC, die in den meisten Fällen nahezu identisch seien. Die Internet-Suchmaschine könne anders als die Gesundheitsbehörde täglich auf aktuelle Daten zurückgreifen. Dadurch sei Google in der Lage, die Grippesaison ein bis zwei Wochen früher vorherzusagen. Und Zeit bedeutet Leben, wie Lyn Finelli sagt, Leiter der Abteilung Influenza der USSeuchenkontrollbehörde: "Je früher wir gewarnt werden, desto früher können wir handeln. Dies kann die Anzahl der Erkrankten erheblich minimieren." "Google Flu Trends" ist das erste Projekt, das Datenbanken einer Suchmaschine nutzt, um einen auftretenden Grippevirus zu lokalisieren - zurzeit nur in den USA, aber weltweite Prognosen wären ein folgerichtiger nächster Schritt. Philip M. Polgreen von der Universität von Iowa verspricht sich noch viel mehr: "Theoretisch können wir diese Flut an Informationen dazu nutzen, auch den Verlauf anderer Krankheiten besser zu studieren." Um das Grippe-Ausbreitungsmodell zu erstellen, hat Google mehrere hundert Milliarden Suchanfragen aus den vergangenen Jahren analysiert. Datenschützer haben den Internetgiganten bereits mehrfach als "datenschutzfeindlich" eingestuft. Die Anwender wüssten weder, was mit den gesammelten Daten passiere, noch wie lange gespeicherte Informationen verfügbar seien. Google versichert jedoch, dass "Flu Trends" die Privatsphäre wahre. Das Tool könne niemals dazu genutzt werden, einzelne Nutzer zu identifizieren, da wir bei der Erstellung der Statistiken lediglich anonyme Datenmaterialien nutzen. Die Muster, die wir in den Daten analysieren, ergeben erst in einem größeren Kontext Sinn." An einer echten Virus-Grippe - nicht zu verwechseln mit einer Erkältung - erkranken weltweit mehrere Millionen Menschen, mehr als 500 000 sterben daran."

Date

3. 5.1997 8:44:22

0.021414658 = product of:
  0.042829316 = sum of:
    0.042829316 = product of:
      0.08565863 = sum of:
        0.08565863 = weight(_text_:22 in 6783) [ClassicSimilarity], result of:
          0.08565863 = score(doc=6783,freq=2.0), product of:
            0.18449724 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052685954 = queryNorm
            0.46428138 = fieldWeight in 6783, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.09375 = fieldNorm(doc=6783)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

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

0.018353842 = product of:
  0.036707684 = sum of:
    0.036707684 = sum of:
      0.015293028 = weight(_text_:m in 1833) [ClassicSimilarity], result of:
        0.015293028 = score(doc=1833,freq=4.0), product of:
          0.13110629 = queryWeight, product of:
            2.4884486 = idf(docFreq=9980, maxDocs=44218)
            0.052685954 = queryNorm
          0.11664603 = fieldWeight in 1833, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            2.4884486 = idf(docFreq=9980, maxDocs=44218)
            0.0234375 = fieldNorm(doc=1833)
      0.021414658 = weight(_text_:22 in 1833) [ClassicSimilarity], result of:
        0.021414658 = score(doc=1833,freq=2.0), product of:
          0.18449724 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052685954 = queryNorm
          0.116070345 = fieldWeight in 1833, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0234375 = fieldNorm(doc=1833)
  0.5 = coord(1/2)

Date

11. 5.2008 19:49:22

Editor

Englert, M. u.a.

Type

m

0.014970168 = product of:
  0.029940335 = sum of:
    0.029940335 = product of:
      0.11976134 = sum of:
        0.11976134 = weight(_text_:authors in 4104) [ClassicSimilarity], result of:
          0.11976134 = score(doc=4104,freq=4.0), product of:
            0.24018547 = queryWeight, product of:
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.052685954 = queryNorm
            0.49862027 = fieldWeight in 4104, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.0546875 = fieldNorm(doc=4104)
      0.25 = coord(1/4)
  0.5 = coord(1/2)

Abstract

With the rapid development of Web 2.0, online reviews have become extremely valuable sources for mining customers' opinions. Fine-grained opinion mining has attracted more and more attention of both applied and theoretical research. In this article, the authors study how to automatically mine product features and opinions from multiple review sources. Specifically, they propose an integration strategy to solve the issue. Within the integration strategy, the authors mine domain knowledge from semistructured reviews and then exploit the domain knowledge to assist product feature extraction and sentiment orientation identification from unstructured reviews. Finally, feature-opinion tuples are generated. Experimental results on real-world datasets show that the proposed approach is effective.

0.014276439 = product of:
  0.028552879 = sum of:
    0.028552879 = product of:
      0.057105757 = sum of:
        0.057105757 = weight(_text_:22 in 1737) [ClassicSimilarity], result of:
          0.057105757 = score(doc=1737,freq=2.0), product of:
            0.18449724 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052685954 = queryNorm
            0.30952093 = fieldWeight in 1737, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0625 = fieldNorm(doc=1737)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

22.11.1998 18:57:22

0.014276439 = product of:
  0.028552879 = sum of:
    0.028552879 = product of:
      0.057105757 = sum of:
        0.057105757 = weight(_text_:22 in 1270) [ClassicSimilarity], result of:
          0.057105757 = score(doc=1270,freq=2.0), product of:
            0.18449724 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052685954 = queryNorm
            0.30952093 = fieldWeight in 1270, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0625 = fieldNorm(doc=1270)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Source

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

0.012831573 = product of:
  0.025663147 = sum of:
    0.025663147 = product of:
      0.10265259 = sum of:
        0.10265259 = weight(_text_:authors in 92) [ClassicSimilarity], result of:
          0.10265259 = score(doc=92,freq=4.0), product of:
            0.24018547 = queryWeight, product of:
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.052685954 = queryNorm
            0.42738882 = fieldWeight in 92, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.046875 = fieldNorm(doc=92)
      0.25 = coord(1/4)
  0.5 = coord(1/2)

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.

0.012831573 = product of:
  0.025663147 = sum of:
    0.025663147 = product of:
      0.10265259 = sum of:
        0.10265259 = weight(_text_:authors in 1326) [ClassicSimilarity], result of:
          0.10265259 = score(doc=1326,freq=4.0), product of:
            0.24018547 = queryWeight, product of:
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.052685954 = queryNorm
            0.42738882 = fieldWeight in 1326, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.046875 = fieldNorm(doc=1326)
      0.25 = coord(1/4)
  0.5 = coord(1/2)

Abstract

The effective use of corporate memory is becoming increasingly important because every aspect of e-business requires access to information repositories. Unfortunately, less-than-satisfying effectiveness in state-of-the-art information-retrieval techniques is well known, even for some of the best search engines such as Google. In this study, the authors resolve this retrieval ineffectiveness problem by developing a new framework for predicting query performance, which is the first step toward better retrieval effectiveness. Specifically, they examine the relationship between query performance and query context. A query context consists of the query itself, the document collection, and the interaction between the two. The authors first analyze the characteristics of query context and develop various features for predicting query performance. Then, they propose a context-sensitive model for predicting query performance based on the characteristics of the query and the document collection. Finally, they validate this model with respect to five real-world collections of documents and demonstrate its utility in routing queries to the correct repository with high accuracy.

0.012616104 = product of:
  0.025232209 = sum of:
    0.025232209 = product of:
      0.050464418 = sum of:
        0.050464418 = weight(_text_:m in 6106) [ClassicSimilarity], result of:
          0.050464418 = score(doc=6106,freq=2.0), product of:
            0.13110629 = queryWeight, product of:
              2.4884486 = idf(docFreq=9980, maxDocs=44218)
              0.052685954 = queryNorm
            0.38491225 = fieldWeight in 6106, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              2.4884486 = idf(docFreq=9980, maxDocs=44218)
              0.109375 = fieldNorm(doc=6106)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Type

m

0.012616104 = product of:
  0.025232209 = sum of:
    0.025232209 = product of:
      0.050464418 = sum of:
        0.050464418 = weight(_text_:m in 246) [ClassicSimilarity], result of:
          0.050464418 = score(doc=246,freq=2.0), product of:
            0.13110629 = queryWeight, product of:
              2.4884486 = idf(docFreq=9980, maxDocs=44218)
              0.052685954 = queryNorm
            0.38491225 = fieldWeight in 246, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              2.4884486 = idf(docFreq=9980, maxDocs=44218)
              0.109375 = fieldNorm(doc=246)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Type

m

0.012491884 = product of:
  0.024983767 = sum of:
    0.024983767 = product of:
      0.049967535 = sum of:
        0.049967535 = weight(_text_:22 in 2908) [ClassicSimilarity], result of:
          0.049967535 = score(doc=2908,freq=2.0), product of:
            0.18449724 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052685954 = 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)
  0.5 = coord(1/2)

Source

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

0.010813803 = product of:
  0.021627607 = sum of:
    0.021627607 = product of:
      0.043255214 = sum of:
        0.043255214 = weight(_text_:m in 3425) [ClassicSimilarity], result of:
          0.043255214 = score(doc=3425,freq=2.0), product of:
            0.13110629 = queryWeight, product of:
              2.4884486 = idf(docFreq=9980, maxDocs=44218)
              0.052685954 = queryNorm
            0.3299248 = fieldWeight in 3425, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              2.4884486 = idf(docFreq=9980, maxDocs=44218)
              0.09375 = fieldNorm(doc=3425)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Type

m

0.010813803 = product of:
  0.021627607 = sum of:
    0.021627607 = product of:
      0.043255214 = sum of:
        0.043255214 = weight(_text_:m in 6075) [ClassicSimilarity], result of:
          0.043255214 = score(doc=6075,freq=2.0), product of:
            0.13110629 = queryWeight, product of:
              2.4884486 = idf(docFreq=9980, maxDocs=44218)
              0.052685954 = queryNorm
            0.3299248 = fieldWeight in 6075, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              2.4884486 = idf(docFreq=9980, maxDocs=44218)
              0.09375 = fieldNorm(doc=6075)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Type

m