Abstract
This paper proposes an efficient method for data mining of generalized association rules on the basis of partial-match retrieval. A generalized association rule is derived from regularities of data patterns, which are found in the database under a given data hierarchy with enough frequencies. The pattern search is a central part of data mining of this type and occupies most of the running time. In this paper, we regard a data pattern as a partial-match query in partial-match retrieval then the pattern search becomes a problem to find partial-match queries of which answers include sufficient number of database records. The proposed method consists of a selective enumeration of candidate queries and an efficient partial-match retrieval using signatures. A signature, which is a bit sequence of fixed length, is associated with data, a record and a query. The answer for a query is fast computed by bit operations among the signatures. The proposed data mining method is realized based on an extended signature method that can deal with a data hierarchy. We also discuss design issues and mathematical properties of the method.
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Matsumoto, K., Hayase, T., Ikeda, N. (1999). Data Mining of Generalized Association Rules Using a Method of Partial-Match Retrieval. In: Arikawa, S., Furukawa, K. (eds) Discovery Science. DS 1999. Lecture Notes in Computer Science(), vol 1721. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46846-3_15
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DOI: https://doi.org/10.1007/3-540-46846-3_15
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