On Computing Condensed Frequent Pattern Bases

Frequent pattern mining has been studied extensively.However, the effectiveness and efficiency of this mining isoften limited, since the number of frequent patterns generatedis often too large. In many applications it is sufficientto generate and examine only frequent patterns with supportfrequency in close-enough approximation instead of in fullprecision. Such a compact but close-enough frequent patternbase is called a condensed frequent patterns-base.In this paper, we propose and examine several alternativesat the design, representation, and implementation ofsuch condensed frequent pattern-bases. A few algorithmsfor computing such pattern-bases are proposed. Their effectivenessat pattern compression and their efficient computationmethods are investigated. A systematic performancestudy is conducted on different kinds of databases,which demonstrates the effectiveness and efficiency of ourapproach at handling frequent pattern mining in largedatabases.