Time-Constrained Sequential Pattern Mining

Sequential pattern mining is one of the important issues in the research of data mining (Agrawal & Srikant, 1995; Ayres, Gehrke, & Yiu, 2002; Han, Pei, & Yan, 2004; Lin & Lee, 2004; Lin & Lee, 2005b; Roddick & Spiliopoulou, 2002). A typical example is a retail database where each record corresponds to a customer’s purchasing sequence, called data sequence. A data sequence is composed of all the customer’s transactions ordered by transaction time. Each transaction is represented by a set of literals indicating the set of items (called itemset) purchased in the transaction. The objective is to find all the frequent sub-sequences (called sequential patterns) in the sequence database. Whether a sub-sequence is frequent or not is determined by its frequency, named support, in the sequence database. An example sequential pattern might be that 40% customers bought PC and printer, followed by the purchase of scanner and graphics-software, and then digital camera. Such a pattern, denoted by <(PC, printer)(scanner, graphics-software)(digital camera)>, has three elements where each element is an itemset. Although the issue is motivated by the retail industry, the mining technique is applicable to domains bearing sequence characteristics, including the analysis of Web traversal patterns, medical treatments, natural disasters, DNA sequences, and so forth. In order to have more accurate results, constraints in addition to the support threshold need to be specified in the mining (Pei, Han, & Wang, 2007; Chen & Yu, 2006; Garofalakis, Rastogi, & Shim, 2002; Lin & Lee, 2005a; Masseglia, Poncelet, & Teisseire, 2004). Most time-independent constraints can be handled, without modifying the fundamental mining algorithm, by a postprocessing on the result of sequential pattern mining without constraints. Time-constraints, however, cannot be managed by retrieving patterns because the support computation of patterns must validate the time attributes for every data sequence in the mining process. Therefore, time-constrained sequential pattern mining (Lin & Lee, 2005a; Lin, Hsueh, & Chang, 2006; Masseglia, Poncelet, & Teisseire, 2004;) is more challenging, and more important in the aspect of temporal relationship discovery, than conventional pattern mining.