| Summary: | 碩士 === 銘傳大學 === 資訊工程學系碩士班 === 94 === Sequential pattern mining is to find sequential behaviors which most customers frequently do in a transaction database. These behaviors are called sequential patterns. There were many papers proposed algorithms for finding all sequential patterns. However, there is a new problem: users may only need some special sequential patterns, for example, the sequential patterns which include certain items or behaviors. If we let users set the items or patterns which they are interested in before mining process, we will save much execution time and the sequential patterns we found can fit the users'' need. The items or patterns which are preset by users are "pattern constraints." We propose an effective algorithm to find all sequential patterns which fit the constraint from the transaction database. In the experimental results cheaper, we use real dataset and synthetic dataset to compare our algorithm with SPIRIT(R) algorithm and Bit-String algorithm. The results show that although our algorithm used more memory than SPIRIT(R) algorithm during the mining process, our algorithm was faster than SPIRIT(R) algorithm. The results also show that our method not only used less memory space than Bit-String algorithm but also was faster than Bit-String algorithm.
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