Knowledge Discovery in Textual Databases: A Concept-Association Mining Approach

The number of scientific publications is exploding as online digital libraries and the World Wide Web grow. MEDLINE, the premier bibliographic database of the National Library of Medicine (NLM) , contains about 18 million records from more than 7,300 different publications dating from 1965; it is growing by about 400,000 citations each year. The explosive growth of information in textual documents creates great need for techniques for knowledge discovery from text collections.

[1]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[2]  Wynne Hsu,et al.  Integrating Classification and Association Rule Mining , 1998, KDD.

[3]  Padhraic Smyth,et al.  Knowledge Discovery and Data Mining: Towards a Unifying Framework , 1996, KDD.

[4]  Rajeev Motwani,et al.  Dynamic itemset counting and implication rules for market basket data , 1997, SIGMOD '97.

[5]  José Palazzo Moreira de Oliveira,et al.  Concept-based knowledge discovery in texts extracted from the Web , 2000, SKDD.

[6]  Martin Ester,et al.  Frequent term-based text clustering , 2002, KDD.

[7]  Wynne Hsu,et al.  Mining association rules with multiple minimum supports , 1999, KDD '99.

[8]  Hinrich Schütze,et al.  Book Reviews: Foundations of Statistical Natural Language Processing , 1999, CL.

[9]  瀬々 潤,et al.  Traversing Itemset Lattices with Statistical Metric Pruning (小特集 「発見科学」及び一般演題) , 2000 .

[10]  Toshinori Munakata,et al.  Knowledge discovery , 1999, Commun. ACM.

[11]  HippJochen,et al.  Algorithms for association rule mining a general survey and comparison , 2000 .

[12]  Edward Omiecinski,et al.  Alternative Interest Measures for Mining Associations in Databases , 2003, IEEE Trans. Knowl. Data Eng..

[13]  Rajeev Motwani,et al.  Beyond market baskets: generalizing association rules to correlations , 1997, SIGMOD '97.

[14]  Marc Weeber,et al.  Using concepts in literature-based discovery: Simulating Swanson's Raynaud-fish oil and migraine-magnesium discoveries , 2001, J. Assoc. Inf. Sci. Technol..

[15]  Ferenc Bodon,et al.  A fast APRIORI implementation , 2003, FIMI.

[16]  Chi-Sheng Shih,et al.  Extracting classification knowledge of Internet documents with mining term associations: a semantic approach , 1998, SIGIR '98.

[17]  Rakesh Agarwal,et al.  Fast Algorithms for Mining Association Rules , 1994, VLDB 1994.

[18]  Shinichi Morishita,et al.  Transversing itemset lattices with statistical metric pruning , 2000, PODS '00.

[19]  Haym Hirsh,et al.  Mining Associations in Text in the Presence of Background Knowledge , 1996, KDD.

[20]  Marti A. Hearst.  Untangling Text Data Mining , 1999, ACL.

[21]  Ido Dagan,et al.  Knowledge Discovery in Textual Databases (KDT) , 1995, KDD.

[22]  Robert Krovetz,et al.  Viewing morphology as an inference process , 1993, Artif. Intell..

[23]  Ido Dagan,et al.  Mining Text Using Keyword Distributions , 1998, Journal of Intelligent Information Systems.

[24]  David D. Lewis,et al.  An evaluation of phrasal and clustered representations on a text categorization task , 1992, SIGIR '92.