An efficient mining of weighted frequent patterns with length decreasing support constraints

摘要

Frequent pattern mining has been extensively studied in the data mining field due to its broad applications in mining association rules, correlations, closed frequent patterns, graph patterns, constraint-based frequent patterns, sequential patterns, and many other data mining tasks. Two concerns exist for frequent pattern mining in the real world. First, each item has different importance so researchers have proposed weighted frequent pattern mining algorithms that reflect the importance of items. Second, previous mining algorithms use a constant support constraint irrespective of the length of discovered patterns. However, short patterns having only a fewer items tend to be interesting if they have high support, while long patterns can still be interesting although their supports are relatively low. Weight and length decreasing support constraints are important constraints, but no mining algorithms consider both constraints. In this paper, we propose weighted frequent pattern mining with length decreasing support constraints. Our main approach is to push weight constraints and length decreasing support constraints into the pattern growth algorithm. For pruning techniques, we propose the notion of the Weighted Smallest Valid Extension (WSVE) property with/without Minimum Weight (MinW). The WSVE property with/without MinW is applied to transaction pruning, node pruning and path pruning to eliminate weighted infrequent patterns earlier. Our approach generates more concise but important weighted frequent patterns with length decreasing support constraints by applying the WSVE property with/without MinW.