Mining Generalized Association Rules for Sequential and Path Data

Authors:
Wolfgang Gaul;Lars Schmidt-Thieme
Affiliations:
-;-
Venue:
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Year:
2001

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Abstract

While association rules for set data se and describe relations between parts of set valued objects completely, association rules for sequential data are restricted by specific interpretations of the subsequence relation: contiguous subsequences describe localfeatures of a sequence valued object, noncontiguous subsequences its global features. We model both types of features with generalized subsequences that describe local deviations by wildcards, and present a new algorithm of Apriori type for mining all generalized subsequences with prescribed minim m support from a given database of sequences. Furthermore we show that the givenalgorithm automatically takes into account an eventually underlying graph structure, i.e., is applicable to path data also.