Search (40 results, page 1 of 2)

0.007423487 = product of:
  0.025982203 = sum of:
    0.006351802 = product of:
      0.03175901 = sum of:
        0.03175901 = weight(_text_:system in 1737) [ClassicSimilarity], result of:
          0.03175901 = score(doc=1737,freq=2.0), product of:
            0.11408355 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03622214 = queryNorm
            0.27838376 = fieldWeight in 1737, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.0625 = fieldNorm(doc=1737)
      0.2 = coord(1/5)
    0.0196304 = product of:
      0.0392608 = sum of:
        0.0392608 = weight(_text_:22 in 1737) [ClassicSimilarity], result of:
          0.0392608 = score(doc=1737,freq=2.0), product of:
            0.12684377 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03622214 = 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)
  0.2857143 = coord(2/7)

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

0.0064955507 = product of:
  0.022734426 = sum of:
    0.0055578267 = product of:
      0.027789133 = sum of:
        0.027789133 = weight(_text_:system in 2908) [ClassicSimilarity], result of:
          0.027789133 = score(doc=2908,freq=2.0), product of:
            0.11408355 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03622214 = queryNorm
            0.2435858 = fieldWeight in 2908, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.0546875 = fieldNorm(doc=2908)
      0.2 = coord(1/5)
    0.0171766 = product of:
      0.0343532 = sum of:
        0.0343532 = weight(_text_:22 in 2908) [ClassicSimilarity], result of:
          0.0343532 = score(doc=2908,freq=2.0), product of:
            0.12684377 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03622214 = 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)
  0.2857143 = coord(2/7)

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

0.00577393 = product of:
  0.020208754 = sum of:
    0.007939752 = product of:
      0.03969876 = sum of:
        0.03969876 = weight(_text_:system in 668) [ClassicSimilarity], result of:
          0.03969876 = score(doc=668,freq=8.0), product of:
            0.11408355 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03622214 = queryNorm
            0.3479797 = fieldWeight in 668, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.0390625 = fieldNorm(doc=668)
      0.2 = coord(1/5)
    0.0122690005 = product of:
      0.024538001 = sum of:
        0.024538001 = weight(_text_:22 in 668) [ClassicSimilarity], result of:
          0.024538001 = score(doc=668,freq=2.0), product of:
            0.12684377 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03622214 = 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)
  0.2857143 = coord(2/7)

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

0.0049076 = product of:
  0.0343532 = sum of:
    0.0343532 = product of:
      0.0687064 = sum of:
        0.0687064 = weight(_text_:22 in 4577) [ClassicSimilarity], result of:
          0.0687064 = score(doc=4577,freq=2.0), product of:
            0.12684377 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03622214 = 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)
  0.14285715 = coord(1/7)

Date

2. 4.2000 18:01:22

0.0038721121 = product of:
  0.027104784 = sum of:
    0.027104784 = product of:
      0.06776196 = sum of:
        0.0439427 = weight(_text_:retrieval in 1067) [ClassicSimilarity], result of:
          0.0439427 = score(doc=1067,freq=8.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.40105087 = fieldWeight in 1067, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.046875 = fieldNorm(doc=1067)
        0.023819257 = weight(_text_:system in 1067) [ClassicSimilarity], result of:
          0.023819257 = score(doc=1067,freq=2.0), product of:
            0.11408355 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03622214 = queryNorm
            0.20878783 = fieldWeight in 1067, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.046875 = fieldNorm(doc=1067)
      0.4 = coord(2/5)
  0.14285715 = coord(1/7)

Abstract

In this paper we combine computer vision and data mining techniques to model high-level concepts for image retrieval, on the basis of basic perceptual features of the human visual system. High-level concepts related to these features are learned and represented by means of a set of fuzzy association rules. The concepts so acquired can be used for image retrieval with the advantage that it is not needed to provide an image as a query. Instead, a query is formulated by using the labels that identify the learned concepts as search terms, and the retrieval process calculates the relevance of an image to the query by an inference mechanism. An additional feature of our methodology is that it can capture user's subjectivity. For that purpose, fuzzy sets theory is employed to measure user's assessments about the fulfillment of a concept by an image.

0.0028043431 = product of:
  0.0196304 = sum of:
    0.0196304 = product of:
      0.0392608 = sum of:
        0.0392608 = weight(_text_:22 in 1270) [ClassicSimilarity], result of:
          0.0392608 = score(doc=1270,freq=2.0), product of:
            0.12684377 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03622214 = 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)
  0.14285715 = coord(1/7)

Source

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

0.002650327 = product of:
  0.018552288 = sum of:
    0.018552288 = product of:
      0.04638072 = sum of:
        0.01830946 = weight(_text_:retrieval in 606) [ClassicSimilarity], result of:
          0.01830946 = score(doc=606,freq=2.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.16710453 = fieldWeight in 606, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0390625 = fieldNorm(doc=606)
        0.028071264 = weight(_text_:system in 606) [ClassicSimilarity], result of:
          0.028071264 = score(doc=606,freq=4.0), product of:
            0.11408355 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03622214 = queryNorm
            0.24605882 = fieldWeight in 606, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.0390625 = fieldNorm(doc=606)
      0.4 = coord(2/5)
  0.14285715 = coord(1/7)

Abstract

With more and more information available on the Internet, the task of making personalized recommendations to assist the user's navigation has become increasingly important. Considering there might be millions of users with different backgrounds accessing a Web site everyday, it is infeasible to build a separate recommendation system for each user. To address this problem, clustering techniques can first be employed to discover user groups. Then, user navigation patterns for each group can be discovered, to allow the adaptation of a Web site to the interest of each individual group. In this paper, we propose to model user access sequences as stochastic processes, and a mixture of Markov models based approach is taken to cluster users and to capture the sequential relationships inherent in user access histories. Several important issues that arise in constructing the Markov models are also addressed. The first issue lies in the complexity of the mixture of Markov models. To improve the efficiency of building/maintaining the mixture of Markov models, we develop a lightweight adapt-ive algorithm to update the model parameters without recomputing model parameters from scratch. The second issue concerns the proper selection of training data for building the mixture of Markov models. We investigate two different training data selection strategies and perform extensive experiments to compare their effectiveness on a real dataset that is generated by a Web-based knowledge management system, Livelink.

Footnote

Beitrag eines Themenschwerpunktes "Mining Web resources for enhancing information retrieval"

0.0026166062 = product of:
  0.018316243 = sum of:
    0.018316243 = product of:
      0.045790605 = sum of:
        0.02197135 = weight(_text_:retrieval in 1611) [ClassicSimilarity], result of:
          0.02197135 = score(doc=1611,freq=2.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.20052543 = fieldWeight in 1611, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.046875 = fieldNorm(doc=1611)
        0.023819257 = weight(_text_:system in 1611) [ClassicSimilarity], result of:
          0.023819257 = score(doc=1611,freq=2.0), product of:
            0.11408355 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03622214 = queryNorm
            0.20878783 = fieldWeight in 1611, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.046875 = fieldNorm(doc=1611)
      0.4 = coord(2/5)
  0.14285715 = coord(1/7)

Abstract

"Garbage in, garbage out" is a well-known phrase in computer analysis, and one that comes to mind when mining Web data to draw conclusions about Web users. The challenge is that data analysts wish to infer patterns of client-side behavior from server-side data. However, because only a fraction of the user's actions ever reaches the Web server, analysts must rely an incomplete data. In this paper, we propose a client-side monitoring system that is unobtrusive and supports flexible data collection. Moreover, the proposed framework encompasses client-side applications beyond the Web browser. Expanding monitoring beyond the browser to incorporate standard office productivity tools enables analysts to derive a much richer and more accurate picture of user behavior an the Web.

Footnote

Teil eines Themenheftes: "Web retrieval and mining: A machine learning perspective"

0.0017527144 = product of:
  0.0122690005 = sum of:
    0.0122690005 = product of:
      0.024538001 = sum of:
        0.024538001 = weight(_text_:22 in 1605) [ClassicSimilarity], result of:
          0.024538001 = score(doc=1605,freq=2.0), product of:
            0.12684377 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03622214 = queryNorm
            0.19345059 = fieldWeight in 1605, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1605)
      0.5 = coord(1/2)
  0.14285715 = coord(1/7)

Source

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

0.0017527144 = product of:
  0.0122690005 = sum of:
    0.0122690005 = product of:
      0.024538001 = sum of:
        0.024538001 = weight(_text_:22 in 5011) [ClassicSimilarity], result of:
          0.024538001 = score(doc=5011,freq=2.0), product of:
            0.12684377 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03622214 = queryNorm
            0.19345059 = fieldWeight in 5011, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5011)
      0.5 = coord(1/2)
  0.14285715 = coord(1/7)

Date

7. 3.2019 16:32:22

0.001384065 = product of:
  0.009688455 = sum of:
    0.009688455 = product of:
      0.04844227 = sum of:
        0.04844227 = weight(_text_:retrieval in 607) [ClassicSimilarity], result of:
          0.04844227 = score(doc=607,freq=14.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.442117 = fieldWeight in 607, product of:
              3.7416575 = tf(freq=14.0), with freq of:
                14.0 = termFreq=14.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0390625 = fieldNorm(doc=607)
      0.2 = coord(1/5)
  0.14285715 = coord(1/7)

Abstract

Understanding what kinds of Web pages are the most useful for Web search engine users is a critical task in Web information retrieval (IR). Most previous works used hyperlink analysis algorithms to solve this problem. However, little research has been focused on query-independent Web data cleansing for Web IR. In this paper, we first provide analysis of the differences between retrieval target pages and ordinary ones based on more than 30 million Web pages obtained from both the Text Retrieval Conference (TREC) and a widely used Chinese search engine, SOGOU (www.sogou.com). We further propose a learning-based data cleansing algorithm for reducing Web pages that are unlikely to be useful for user requests. We found that there exists a large proportion of low-quality Web pages in both the English and the Chinese Web page corpus, and retrieval target pages can be identified using query-independent features and cleansing algorithms. The experimental results showed that our algorithm is effective in reducing a large portion of Web pages with a small loss in retrieval target pages. It makes it possible for Web IR tools to meet a large fraction of users' needs with only a small part of pages on the Web. These results may help Web search engines make better use of their limited storage and computation resources to improve search performance.

Footnote

Beitrag eines Themenschwerpunktes "Mining Web resources for enhancing information retrieval"

0.001384065 = product of:
  0.009688455 = sum of:
    0.009688455 = product of:
      0.04844227 = sum of:
        0.04844227 = weight(_text_:retrieval in 3607) [ClassicSimilarity], result of:
          0.04844227 = score(doc=3607,freq=14.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.442117 = fieldWeight in 3607, product of:
              3.7416575 = tf(freq=14.0), with freq of:
                14.0 = termFreq=14.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3607)
      0.2 = coord(1/5)
  0.14285715 = coord(1/7)

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.

0.0012832579 = product of:
  0.008982805 = sum of:
    0.008982805 = product of:
      0.044914022 = sum of:
        0.044914022 = weight(_text_:system in 6759) [ClassicSimilarity], result of:
          0.044914022 = score(doc=6759,freq=4.0), product of:
            0.11408355 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03622214 = queryNorm
            0.3936941 = fieldWeight in 6759, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.0625 = fieldNorm(doc=6759)
      0.2 = coord(1/5)
  0.14285715 = coord(1/7)

Abstract

Examines the relationship between systems and users from the knowledge discovery in databases or data mining perspecitives. A comprehensive study on knowledge discovery in human computer symbiosis is needed. Proposes a database-user adversarial partnership, which is general enough to cover knowledge discovery and security of issues related to databases and their users. It can be further generalized into system-user adversarial paertnership. Discusses opportunities provided by knowledge discovery techniques and potential social implications

0.0011787476 = product of:
  0.008251233 = sum of:
    0.008251233 = product of:
      0.041256163 = sum of:
        0.041256163 = weight(_text_:system in 2952) [ClassicSimilarity], result of:
          0.041256163 = score(doc=2952,freq=6.0), product of:
            0.11408355 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03622214 = queryNorm
            0.36163113 = fieldWeight in 2952, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.046875 = fieldNorm(doc=2952)
      0.2 = coord(1/5)
  0.14285715 = coord(1/7)

Abstract

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

0.0010873 = product of:
  0.0076110996 = sum of:
    0.0076110996 = product of:
      0.0380555 = sum of:
        0.0380555 = weight(_text_:retrieval in 1326) [ClassicSimilarity], result of:
          0.0380555 = score(doc=1326,freq=6.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.34732026 = fieldWeight in 1326, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.046875 = fieldNorm(doc=1326)
      0.2 = coord(1/5)
  0.14285715 = coord(1/7)

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.

0.0010357393 = product of:
  0.007250175 = sum of:
    0.007250175 = product of:
      0.036250874 = sum of:
        0.036250874 = weight(_text_:retrieval in 600) [ClassicSimilarity], result of:
          0.036250874 = score(doc=600,freq=4.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.33085006 = fieldWeight in 600, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0546875 = fieldNorm(doc=600)
      0.2 = coord(1/5)
  0.14285715 = coord(1/7)

Footnote

Einführung in einen Themenschwerpunkt "Mining Web resources for enhancing information retrieval"

9.822896E-4 = product of:
  0.0068760267 = sum of:
    0.0068760267 = product of:
      0.034380134 = sum of:
        0.034380134 = weight(_text_:system in 2533) [ClassicSimilarity], result of:
          0.034380134 = score(doc=2533,freq=6.0), product of:
            0.11408355 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03622214 = queryNorm
            0.30135927 = fieldWeight in 2533, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2533)
      0.2 = coord(1/5)
  0.14285715 = coord(1/7)

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.

9.624433E-4 = product of:
  0.0067371028 = sum of:
    0.0067371028 = product of:
      0.033685513 = sum of:
        0.033685513 = weight(_text_:system in 916) [ClassicSimilarity], result of:
          0.033685513 = score(doc=916,freq=4.0), product of:
            0.11408355 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03622214 = queryNorm
            0.29527056 = fieldWeight in 916, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.046875 = fieldNorm(doc=916)
      0.2 = coord(1/5)
  0.14285715 = coord(1/7)

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.

9.624433E-4 = product of:
  0.0067371028 = sum of:
    0.0067371028 = product of:
      0.033685513 = sum of:
        0.033685513 = weight(_text_:system in 4226) [ClassicSimilarity], result of:
          0.033685513 = score(doc=4226,freq=4.0), product of:
            0.11408355 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03622214 = queryNorm
            0.29527056 = fieldWeight in 4226, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.046875 = fieldNorm(doc=4226)
      0.2 = coord(1/5)
  0.14285715 = coord(1/7)

Abstract

Many powerful methods and tools exist for extracting meaning from scientific publications, their texts, and their citation links. However, existing proposals often neglect a fundamental aspect of learning: that understanding and learning require an active and constructive exploration of a domain. In this paper, we describe a new method and a tool that use data mining and interactivity to turn the typical search and retrieve dialogue, in which the user asks questions and a system gives answers, into a dialogue that also involves sense-making, in which the user has to become active by constructing a bibliography and a domain model of the search term(s). This model starts from an automatically generated and annotated clustering solution that is iteratively modified by users. The tool is part of an integrated authoring system covering all phases from search through reading and sense-making to writing. Two evaluation studies demonstrate the usability of this interactive and constructive approach, and they show that clusters and groups represent identifiable sub-topics.

9.0740033E-4 = product of:
  0.006351802 = sum of:
    0.006351802 = product of:
      0.03175901 = sum of:
        0.03175901 = weight(_text_:system in 6625) [ClassicSimilarity], result of:
          0.03175901 = score(doc=6625,freq=2.0), product of:
            0.11408355 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03622214 = queryNorm
            0.27838376 = fieldWeight in 6625, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.0625 = fieldNorm(doc=6625)
      0.2 = coord(1/5)
  0.14285715 = coord(1/7)

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