Frequent pattern mining has been studied extensively. However, the effectiveness and efficiency of this mining is often limited, since the number of frequent patterns generated is often too large. In many applications it is sufficient to generate and examine only frequent patterns with support frequency in close-enough approximation instead of in full precision. Such a compact but close-enough frequent pattern base is called a condensed frequent patterns-base.
In this paper, we propose and examine several alternatives at the design, representation, and implementation of such condensed frequent pattern-bases. A few algorithms for computing such pattern-bases are proposed. Their effectiveness at pattern compression and their efficient computation methods are investigated. A systematic performance study is conducted on different kinds of databases, which demonstrates the effectiveness and efficiency of our approach at handling frequent pattern mining in large databases.
& Han, J.
(2002). On Computing Condensed Frequent Pattern Bases. Proceedings of the 2002 IEEE International Conference on Data Mining, 378-385.