A Rough Set Model for Constraint Driven Mining of Sequential Patterns

Data mining and knowledge discovery methods host many decision support and engineering application needs of various organizations. Most real world data has time component inherent in them. Sequential patterns are inter-eve nt patterns ordered in time associated with various objects unde r study. Analysis and discovery of frequent sequential patter ns in user defined constraints are interesting data mining res ults. These patterns can serve a variety of enterprise applicat ions concerning analytic and decision support needs. Imposition of various constraints further enhances the quality of mining results and restricts the results to only relevant patterns. I n this paper, we have proposed a rough set perspective to the problem of constraint driven mining of sequential patterns. We have used indiscernibility relation from theory of rough sets to partition the search space of sequential patterns and have proposed a novel algorithm that allows pre-visualization of patterns and imposition of various types of constraints in the mining task. The algorithm C-Rough Set Partitioning is at least ten times faster than the naive algorithm SPRINT that is based on imposing regular expression constraints.

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