Mining Frequent Patterns with Multiple Item Support Thresholds in Tourism Information Databases

Abstract

Frequent pattern mining is an important model in data mining. Certain frequent patterns with low minimum support can provide useful information in many real datasets. However, the predefined minimum support value as a threshold needs to be set properly, or it may cause rare item problem. A too high threshold causes missing of rare items, whereas a too low threshold causes combinatorial explosion. In this paper, we proposed an improved FP-growth based approach to solve the rare item problem with multiple item supports, where each item has its own minimum support. Considering the difficulty of setting appropriate thresholds for all items, an automatic tuning multiple item support (MIS) approach is proposed, which is based on Central Limit Theorem. A series of experimental results on various tourism information datasets shows that the proposed approach can enhance frequent pattern mining with better efficiency and efficacy.