Data mining is to find valid, novel, potentially useful, and ultimately understandable patterns in data. In general, there are many kinds of patterns that can be discovered from data. For example, association rules can be mined for market basket analysis, classification rules can be found for accurate classifiers, clusters and outliers can be identified for customer relation management. Frequent pattern mining plans an essential role in many data mining tasks, such as mining association rules, correlations, causality, sequential patterns, episodes, multi-dimensional patterns, Maa-patterns, partial periodicity, and emerging patterns. Frequent pattern mining techniques can also be extended to solve many other problems, such as iceberg-cube computation and classification. Thus, effective and efficient frequent pattern mining is an important and interesting research problem.