Incremental Mining on Association Rules

Abstract

The discovery of association rules has been known to be useful in selective marketing, decision analysis, and business management. An important application area of mining association rules is the market basket analysis, which studies the buying behaviors of customers by searching for sets of items that are frequently purchased together. With the increasing use of the record-based databases whose data is being continuously added, recent important applications have called for the need of incremental mining. In dynamic transaction databases, new transactions are appended and obsolete transactions are discarded as time advances. Several research works have developed feasible algorithms for deriving precise association rules efficiently and effectively in such dynamic databases. On the other hand, approaches to generate approximations from data streams have received a significant amount of research attention recently. In each scheme, previously proposed algorithms are explored with examples to illustrate their concepts and techniques in this chapter.