Title :
Mining generalized association rules for sequential and path data
Author :
Gaul, Wolfgang ; Schmidt-Thieme, Lars
Author_Institution :
Inst. fur Entscheidungstheorie und Unternehmensforschung, Karlsruhe Univ., Germany
Abstract :
While association rules for set data use 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 local features of a sequence valued object, noncontiguous subsequences its global features. We model both types of features with generalized subsequences that describe local deviations by wild cards, and present a new algorithm of a priori type for mining all generalized subsequences with prescribed minimum support from a given database of sequences. Furthermore we show that the given algorithm automatically takes into account an eventually underlying graph structure, i.e., is applicable to path data also
Keywords :
data mining; database theory; sequences; contiguous subsequences; data mining; database sequences; generalized association rule mining; global features; graph structure; local features; noncontiguous subsequences; path data; sequence valued object; sequential data; set valued objects; subsequence relation; wild cards; Algorithm design and analysis; Association rules; Data mining; Databases; State estimation;
Conference_Titel :
Data Mining, 2001. ICDM 2001, Proceedings IEEE International Conference on
Conference_Location :
San Jose, CA
Print_ISBN :
0-7695-1119-8
DOI :
10.1109/ICDM.2001.989573