Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining knowledge from text using information extraction
ACM SIGKDD Explorations Newsletter - Natural language processing and text mining
On discovery of soft associations with "most" fuzzy quantifier for item promotion applications
Information Sciences: an International Journal
A retrieval strategy using the integrated knowledge of similarity and associations
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications: Part II
A case retrieval approach using similarity and association knowledge
OTM'11 Proceedings of the 2011th Confederated international conference on On the move to meaningful internet systems - Volume Part I
Retrieval in CBR using a combination of similarity and association knowledge
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
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Variation and noise in database entries can prevent data mining algorithms, such as association rule mining, from discovering important regularities. In particular, textual fields can exhibit variation due to typographical errors, mispellings, abbreviations, etc.. By allowing partial or "soft matching" of items based on a similarity metric such as edit-distance or cosine similarity, additional important patterns can be detected. This paper introduces an algorithm, SoftApriori that discovers soft-matching association rules given a user-supplied similarity metric for each field. Experimental results on several "noisy" datasets extracted from text demonstrate that SoftApriori discovers additional relationships that more accurately reflect regularities in the data.