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The problem addressed in this paper is to discover the frequently occurred sequential patterns from databases. Although much work has been devoted to this subject, to the best of our knowledge, no previous research was able to find sequential patterns from d-dimensional sequence data, where d>2. Without such a capability, many practical data would be impossible to mine. For example, an online stock-trading site may have a customer database, where each customer may visit a Web site in a series of days; each day takes a series of sessions and each session visits a series of Web pages. Then, the data for each customer forms a 3-dimensional list, where the first dimension is days, the second is sessions, and the third is visited pages. To mine sequential patterns from this kind of sequence data, two efficient algorithms have been developed in this paper.
Frequent pattern, sequential patterns, sequence data, data mining.
Chung-Ching Yu, Yen-Liang Chen, "Mining Sequential Patterns from Multidimensional Sequence Data", IEEE Transactions on Knowledge & Data Engineering, vol. 17, no. , pp. 136-140, January 2005, doi:10.1109/TKDE.2005.13
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