Abstract
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.
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References
Agrawal R and Srikant R (1994) Fast Algorithms for Mining Association Rules. 20th International Conference on Very Large Data Bases, Santiago de Chile, Chile, pp 487-499.
Agrawal R, Imielinski T, and Swami A (1993) Mining Association Rules Between Sets of Items in Large Database. ACM SIGMOD Conference, pp. 207-216.
Beil F, Ester M, and Xu X (2002) Frequent Term-Based Text Clustering. 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Alberta, Canada, pp 436-442.
BioMed Central text corpus, http://www.biomedcentral.com/info/about/datamining/.
Bodon F (2003) A Fast APRIORI Implementation. IEEE ICDM Workshop on Frequent Itemset Mining Implementations, Melbourne, Florida, pp 56-65.
Brin S, Motwani R, and Silverstein C (1997) Beyond Market Basket: Generalizing Association Rules to Correlations. ACM SIGMOD Conference, Tucson, Arizona, pp. 265-276.
Brin S, Motwani R, Ullman J, and Tsur, S (1997) Dynamic Itemset Counting and Implication Rules for Market Basket Data ACM SIGMOD Conference, Tucson, Arizona, pp. 255-264.
College of Biological Sciences, http://www.biosci.ohio-state.edu/∼parasite/plasmodium.html.
Fayyad U, Piatetsky-Shapiro G, and Smyth P (1996) Knowledge Discovery and Data Mining: Towards a Unifying Framework, Second Int. Conf. on Knowledge Discovery and Data Mining, Portland, OR, pp. 82-88.
Feldman R and Dagan I (1995) Knowledge Discovery in Textual Databases (KDT). First international conference on knowledge discovery (KDD'95), Montreal, pp 112-117.
Feldman R, and Hirsh H (1996) Mining Associations In Text In The Presence Of Background Knowledge. 2nd International Conference on Knowledge Discovery and Data Mining, pp. 343-346.
Feldman R, Dagan I, and Hirsh H (1998) Mining Text Using Keyword Distributions. Journal of Intelligent Information Systems: Integrating Artificial Intelligence and Database Technologies, 10(3), pp. 281-300.
Health Information Main Page, http://www.niams.nih.gov/hi/topics/psoriasis/psoriafs.htm.
Hearst MA (1999) Untangling Text Data Mining. 37th Annual Meeting of the Association for Computational Linguistics (ACL'99), University of Maryland, pp 3-10.
Hipp J, Guntzer U, and Nakhaeizadeh G (2000) Algorithms for Association Rule Mining – A General Survey and Comparison, ACM SIGKDD Explorations, Vol.2, pp. 58-64.
Hospital for Special Surgery, Orthopedic Surgery, http://orthopaedics.hss.edu/services/conditions/hip/dv_thrombosis.asp.
Information For Health Professionals http://allhat.sph.uth.tmc.edu.
Krovetz R (1993) Viewing Morphology as an Inference Process. 16th ACM SIGIR Conference, Pittsburgh, pp 191-202.
Lewis D (1992) An Evaluation Of Phrasal And Clustered Representations On A Text Categorization Problem. ACM-SIGIR Conference on Information Retrieval, Copenhagen, Denmark, pp 37-50.
Lin SH, Shih CS, and Chen MC (1998) Extracting Classification Knowledge of Internet Documents with Mining Term Associations: A Semantic Approach. 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Melbourne, Australia, pp 241-249.
Liu B, Hsu W, and Ma YM (1998) Integrating Classification and Association Rule Mining. The Fourth International Conference on Knowledge Discovery and Data Mining (KDD-98), New York City, pp. 80-86.
Liu B, Hsu W, and Ma YM (1997) Mining Association Rules with Multiple Minimum Supports. The Fourth International Conference on Knowledge Discovery and Data Mining, pp. 337-341.
Loh S, Wives LK, and Oliveia JPM (2000) Concept Based Knowledge Discovery from Texts Extracted from the Web. ACM SIGKDD Explorations, vol. 2, pp. 29-40.
Morishita S and Sese J (2000) Traversing Itemset Lattices with Statistical Metric Pruning. In Proc. of the 19th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems,. ACM Press, pp. 226-236.
National Center for Biotechnology Information-A, retrieved from http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Abstract&list_uids=9662402.
National Center for Biotechnology Information-B, retrieved from http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=12150501&dopt=Abstract.
Omiecinski E (2003) Alternative Interest Measures for Mining Associations. IEEE Trans. Knowledge and Data Engineering, 15(1), pp. 57-69.
PubMed Central, retrieved from http://www.pubmedcentral.nih.gov/.
Reuters-21578 Text Categorization Text Collection retrieved from http://www.daviddlewis.com/resources/testcollections/reuters21578/.
Weeber M, Vos R, Klein H, de Jong-van den Berg LTW (2001) Using Concepts In Literature-Based Discovery: Simulating Swanson’S Raynaud Fish Oil And Migraine Magnesium Discoveries. Journal of American Society for Information Science and Technology; 52 (7), pp.548–557.
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Mete, M., Yuruk, N., Xu, X., Berleant, D. (2009). Knowledge Discovery in Textual Databases: A Concept-Association Mining Approach. In: Chan, Y., Talburt, J., Talley, T. (eds) Data Engineering. International Series in Operations Research & Management Science, vol 132. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-0176-7_11
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