Search (60 results, page 1 of 3)

0.023324974 = product of:
  0.081637405 = sum of:
    0.068408266 = weight(_text_:great in 668) [ClassicSimilarity], result of:
      0.068408266 = score(doc=668,freq=2.0), product of:
        0.21992016 = queryWeight, product of:
          5.6307793 = idf(docFreq=430, maxDocs=44218)
          0.03905679 = queryNorm
        0.31105953 = fieldWeight in 668, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.6307793 = idf(docFreq=430, maxDocs=44218)
          0.0390625 = fieldNorm(doc=668)
    0.013229139 = product of:
      0.026458278 = sum of:
        0.026458278 = weight(_text_:22 in 668) [ClassicSimilarity], result of:
          0.026458278 = score(doc=668,freq=2.0), product of:
            0.13677022 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03905679 = 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

0.0225025 = product of:
  0.07875875 = sum of:
    0.047008816 = weight(_text_:based in 6783) [ClassicSimilarity], result of:
      0.047008816 = score(doc=6783,freq=2.0), product of:
        0.11767787 = queryWeight, product of:
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.03905679 = queryNorm
        0.39947033 = fieldWeight in 6783, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.09375 = fieldNorm(doc=6783)
    0.031749934 = product of:
      0.06349987 = sum of:
        0.06349987 = weight(_text_:22 in 6783) [ClassicSimilarity], result of:
          0.06349987 = score(doc=6783,freq=2.0), product of:
            0.13677022 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03905679 = 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)
  0.2857143 = coord(2/7)

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

Source

Knowledge-based systems. 10(1998) no.7, S.401-470

0.021285411 = product of:
  0.14899787 = sum of:
    0.14899787 = weight(_text_:businesses in 248) [ClassicSimilarity], result of:
      0.14899787 = score(doc=248,freq=2.0), product of:
        0.29628533 = queryWeight, product of:
          7.5860133 = idf(docFreq=60, maxDocs=44218)
          0.03905679 = queryNorm
        0.5028864 = fieldWeight in 248, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          7.5860133 = idf(docFreq=60, maxDocs=44218)
          0.046875 = fieldNorm(doc=248)
  0.14285715 = coord(1/7)

Abstract

Das Informationspapier beschreibt die digitale Nachverfolgung von wissenschaftlichen Aktivitäten. Wissenschaftlerinnen und Wissenschaftler nutzen täglich eine Vielzahl von digitalen Informationsressourcen wie zum Beispiel Literatur- und Volltextdatenbanken. Häufig fallen dabei Nutzungsspuren an, die Aufschluss geben über gesuchte und genutzte Inhalte, Verweildauern und andere Arten der wissenschaftlichen Aktivität. Diese Nutzungsspuren können von den Anbietenden der Informationsressourcen festgehalten, aggregiert und weiterverwendet oder verkauft werden. Das Informationspapier legt die Transformation von Wissenschaftsverlagen hin zu Data Analytics Businesses dar, verweist auf die Konsequenzen daraus für die Wissenschaft und deren Einrichtungen und benennt die zum Einsatz kommenden Typen der Datengewinnung. Damit dient es vor allem der Darstellung gegenwärtiger Praktiken und soll zu Diskussionen über deren Konsequenzen für die Wissenschaft anregen. Es richtet sich an alle Wissenschaftlerinnen und Wissenschaftler sowie alle Akteure in der Wissenschaftslandschaft.

0.015001667 = product of:
  0.052505832 = sum of:
    0.03133921 = weight(_text_:based in 1737) [ClassicSimilarity], result of:
      0.03133921 = score(doc=1737,freq=2.0), product of:
        0.11767787 = queryWeight, product of:
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.03905679 = queryNorm
        0.26631355 = fieldWeight in 1737, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.0625 = fieldNorm(doc=1737)
    0.021166623 = product of:
      0.042333245 = sum of:
        0.042333245 = weight(_text_:22 in 1737) [ClassicSimilarity], result of:
          0.042333245 = score(doc=1737,freq=2.0), product of:
            0.13677022 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03905679 = 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

0.0116941 = product of:
  0.040929347 = sum of:
    0.02770021 = weight(_text_:based in 1605) [ClassicSimilarity], result of:
      0.02770021 = score(doc=1605,freq=4.0), product of:
        0.11767787 = queryWeight, product of:
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.03905679 = queryNorm
        0.23539014 = fieldWeight in 1605, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1605)
    0.013229139 = product of:
      0.026458278 = sum of:
        0.026458278 = weight(_text_:22 in 1605) [ClassicSimilarity], result of:
          0.026458278 = score(doc=1605,freq=2.0), product of:
            0.13677022 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03905679 = 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)
  0.2857143 = coord(2/7)

Abstract

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

Source

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

0.007834803 = product of:
  0.05484362 = sum of:
    0.05484362 = weight(_text_:based in 3835) [ClassicSimilarity], result of:
      0.05484362 = score(doc=3835,freq=2.0), product of:
        0.11767787 = queryWeight, product of:
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.03905679 = queryNorm
        0.46604872 = fieldWeight in 3835, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.109375 = fieldNorm(doc=3835)
  0.14285715 = coord(1/7)

Source

Knowledge-based systems. 14(2001) nos.1/2, S.37-53

0.006785138 = product of:
  0.047495965 = sum of:
    0.047495965 = weight(_text_:based in 2910) [ClassicSimilarity], result of:
      0.047495965 = score(doc=2910,freq=6.0), product of:
        0.11767787 = queryWeight, product of:
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.03905679 = queryNorm
        0.40361002 = fieldWeight in 2910, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.0546875 = fieldNorm(doc=2910)
  0.14285715 = coord(1/7)

Abstract

Examines basic issues of data mining using the theory of rough sets, which is a recent proposal for generalizing classical set theory. The Pawlak rough set model is based on the concept of an equivalence relation. A generalized rough set model need not be based on equivalence relation axioms. The Pawlak rough set model has been used for deriving deterministic as well as probabilistic rules froma complete database. Demonstrates that a generalised rough set model can be used for generating rules from incomplete databases. These rules are based on plausability functions proposed by Shafer. Discusses the importance of rule extraction from incomplete databases in data mining

0.0067155454 = product of:
  0.047008816 = sum of:
    0.047008816 = weight(_text_:based in 4286) [ClassicSimilarity], result of:
      0.047008816 = score(doc=4286,freq=8.0), product of:
        0.11767787 = queryWeight, product of:
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.03905679 = queryNorm
        0.39947033 = fieldWeight in 4286, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.046875 = fieldNorm(doc=4286)
  0.14285715 = coord(1/7)

Abstract

Geolocated social media data provide a powerful source of information about places and regional human behavior. Because only a small amount of social media data have been geolocation-annotated, inference techniques play a substantial role to increase the volume of annotated data. Conventional research in this area has been based on the text content of posts from a given user or the social network of the user, with some recent crossovers between the text- and network-based approaches. This paper proposes a novel approach to categorize highly-mentioned users (celebrities) into Local and Global types, and consequently use Local celebrities as location indicators. A label propagation algorithm is then used over the refined social network for geolocation inference. Finally, we propose a hybrid approach by merging a text-based method as a back-off strategy into our network-based approach. Empirical experiments over three standard Twitter benchmark data sets demonstrate that our approach outperforms state-of-the-art user geolocation methods.

0.005815833 = product of:
  0.04071083 = sum of:
    0.04071083 = weight(_text_:based in 4097) [ClassicSimilarity], result of:
      0.04071083 = score(doc=4097,freq=6.0), product of:
        0.11767787 = queryWeight, product of:
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.03905679 = queryNorm
        0.34595144 = fieldWeight in 4097, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.046875 = fieldNorm(doc=4097)
  0.14285715 = coord(1/7)

Abstract

A challenge of managing and extracting useful knowledge from social media data sources has attracted much attention from academic and industry. To address this challenge, semantic analysis of textual data is focused in this paper. We propose an ontology-based approach to extract semantics of textual data and define the domain of data. In other words, we semantically analyse the social data at two levels i.e. the entity level and the domain level. We have chosen Twitter as a social channel challenge for a purpose of concept proof. Domain knowledge is captured in ontologies which are then used to enrich the semantics of tweets provided with specific semantic conceptual representation of entities that appear in the tweets. Case studies are used to demonstrate this approach. We experiment and evaluate our proposed approach with a public dataset collected from Twitter and from the politics domain. The ontology-based approach leverages entity extraction and concept mappings in terms of quantity and accuracy of concept identification.

0.005815833 = product of:
  0.04071083 = sum of:
    0.04071083 = weight(_text_:based in 720) [ClassicSimilarity], result of:
      0.04071083 = score(doc=720,freq=6.0), product of:
        0.11767787 = queryWeight, product of:
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.03905679 = queryNorm
        0.34595144 = fieldWeight in 720, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.046875 = fieldNorm(doc=720)
  0.14285715 = coord(1/7)

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.

0.005540042 = product of:
  0.038780294 = sum of:
    0.038780294 = weight(_text_:based in 3821) [ClassicSimilarity], result of:
      0.038780294 = score(doc=3821,freq=4.0), product of:
        0.11767787 = queryWeight, product of:
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.03905679 = queryNorm
        0.3295462 = fieldWeight in 3821, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.0546875 = fieldNorm(doc=3821)
  0.14285715 = coord(1/7)

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

0.005540042 = product of:
  0.038780294 = sum of:
    0.038780294 = weight(_text_:based in 3886) [ClassicSimilarity], result of:
      0.038780294 = score(doc=3886,freq=4.0), product of:
        0.11767787 = queryWeight, product of:
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.03905679 = queryNorm
        0.3295462 = fieldWeight in 3886, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.0546875 = fieldNorm(doc=3886)
  0.14285715 = coord(1/7)

Abstract

The paper investigates the acceleration of t-SNE-an embedding technique that is commonly used for the visualization of high-dimensional data in scatter plots-using two tree-based algorithms. In particular, the paper develops variants of the Barnes-Hut algorithm and of the dual-tree algorithm that approximate the gradient used for learning t-SNE embeddings in O(N*logN). Our experiments show that the resulting algorithms substantially accelerate t-SNE, and that they make it possible to learn embeddings of data sets with millions of objects. Somewhat counterintuitively, the Barnes-Hut variant of t-SNE appears to outperform the dual-tree variant.

0.005291656 = product of:
  0.03704159 = sum of:
    0.03704159 = product of:
      0.07408318 = sum of:
        0.07408318 = weight(_text_:22 in 4577) [ClassicSimilarity], result of:
          0.07408318 = score(doc=4577,freq=2.0), product of:
            0.13677022 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03905679 = 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

0.0048465277 = product of:
  0.033925693 = sum of:
    0.033925693 = weight(_text_:based in 3607) [ClassicSimilarity], result of:
      0.033925693 = score(doc=3607,freq=6.0), product of:
        0.11767787 = queryWeight, product of:
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.03905679 = queryNorm
        0.28829288 = fieldWeight in 3607, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3607)
  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.

0.0047486075 = product of:
  0.03324025 = sum of:
    0.03324025 = weight(_text_:based in 2912) [ClassicSimilarity], result of:
      0.03324025 = score(doc=2912,freq=4.0), product of:
        0.11767787 = queryWeight, product of:
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.03905679 = queryNorm
        0.28246817 = fieldWeight in 2912, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.046875 = fieldNorm(doc=2912)
  0.14285715 = coord(1/7)

Abstract

Presents a heuristic, attribute-based, noise-tolerant data mining program, HCV (Version 2.0), absed on the newly-developed extension matrix approach. Gives a simple example of attribute-based induction to show the difference between the rules in variable-valued logic produced by HCV, the decision tree generated by C4.5 and the decision tree's decompiled rules by C4.5 rules. Outlines the extension matrix approach for data mining. Describes the HCV algorithm in detail. Outlines techniques developed and implemented in the HCV program for noise handling and discretization of continuous domains respectively. Follows these with a performance comparison of HCV with famous ID3-like algorithms including C4.5 and C4.5 rules on a collection of standard databases including the famous MONK's problems

0.0047486075 = product of:
  0.03324025 = sum of:
    0.03324025 = weight(_text_:based in 916) [ClassicSimilarity], result of:
      0.03324025 = score(doc=916,freq=4.0), product of:
        0.11767787 = queryWeight, product of:
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.03905679 = queryNorm
        0.28246817 = fieldWeight in 916, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.046875 = fieldNorm(doc=916)
  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.

0.0047486075 = product of:
  0.03324025 = sum of:
    0.03324025 = weight(_text_:based in 3015) [ClassicSimilarity], result of:
      0.03324025 = score(doc=3015,freq=4.0), product of:
        0.11767787 = queryWeight, product of:
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.03905679 = queryNorm
        0.28246817 = fieldWeight in 3015, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.046875 = fieldNorm(doc=3015)
  0.14285715 = coord(1/7)

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.

0.00447703 = product of:
  0.03133921 = sum of:
    0.03133921 = weight(_text_:based in 6625) [ClassicSimilarity], result of:
      0.03133921 = score(doc=6625,freq=2.0), product of:
        0.11767787 = queryWeight, product of:
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.03905679 = queryNorm
        0.26631355 = fieldWeight in 6625, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.0625 = fieldNorm(doc=6625)
  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

0.003957173 = product of:
  0.02770021 = sum of:
    0.02770021 = weight(_text_:based in 5083) [ClassicSimilarity], result of:
      0.02770021 = score(doc=5083,freq=4.0), product of:
        0.11767787 = queryWeight, product of:
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.03905679 = queryNorm
        0.23539014 = fieldWeight in 5083, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5083)
  0.14285715 = coord(1/7)

Abstract

In this paper, we discuss Conceptual Knowledge Discovery in Databases (CKDD) in its connection with Data Analysis. Our approach is based on Formal Concept Analysis, a mathematical theory which has been developed and proven useful during the last 20 years. Formal Concept Analysis has led to a theory of conceptual information systems which has been applied by using the management system TOSCANA in a wide range of domains. In this paper, we use such an application in database marketing to demonstrate how methods and procedures of CKDD can be applied in Data Analysis. In particular, we show the interplay and integration of data mining and data analysis techniques based on Formal Concept Analysis. The main concern of this paper is to explain how the transition from data to knowledge can be supported by a TOSCANA system. To clarify the transition steps we discuss their correspondence to the five levels of knowledge representation established by R. Brachman and to the steps of empirically grounded theory building proposed by A. Strauss and J. Corbin

0.003957173 = product of:
  0.02770021 = sum of:
    0.02770021 = weight(_text_:based in 1867) [ClassicSimilarity], result of:
      0.02770021 = score(doc=1867,freq=4.0), product of:
        0.11767787 = queryWeight, product of:
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.03905679 = queryNorm
        0.23539014 = fieldWeight in 1867, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1867)
  0.14285715 = coord(1/7)

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.