Mining Weighted Sequential Patterns Based on Prefix-Tree and Prism Encoding

Abstract

The purpose of mining sequential patterns problem with weighted constraints is to find high-valued patterns, including infrequent patterns but having items which appear in the pattern of high importance in the sequence database (SD). Therefore, weighted sequential pattern mining will collect a set of more complete patterns with items of low support but of high importance. This paper proposes a new algorithm called WSPM_PreTree to find highly weighted sequential patterns. To collect a set of complete sequential patterns with the stricter weighted constraints of sequential patterns, the proposed algorithm uses both the minimum support constraint and the actual values of items appearing in the SD. To increase the performance of the finding weighted sequential patterns process, the algorithm uses the parent–child relationship on the prefix tree structure to create candidates and combines the weighted mean of the sequential 1-patterns that is calculated from the actual value of items in the SD as conditions to find the weighted sequential patterns. Experimental results show that the proposed algorithm is more efficient than sequential patterns mining with weight constraint (SPMW) algorithm [Ref. 20 ] in the runtime.