| Summary: | 碩士 === 朝陽科技大學 === 資訊管理系碩士班 === 90 === Data Mining has become one of the fast growing areas of research in recent years. Besides association rules mining, researchers endeavor to develop mining methods with time factor considered. Popular research topics include customers buying patterns analysis, Internet surfing sequence analysis, trend analysis, and so on. When probing the customers buying sequential patterns, most developed mining methods require repeated database scans to generate candidate patterns, which are then checked to find frequent sequential patterns. It therefore deteriorates the performances of these methods. This paper presents a Frequent Pattern Adjacent Matrix (FPAM) to record intermediate length-2 patterns. After finding the frequent patterns, it only needs one more round of database scan to find all the sequential patterns by taking advantages of FPAM. Without generating unnecessary patterns, the proposed method is an efficient method for mining frequent sequential patterns from databases.
However, the existence of patterns is often related to the circumstances or conditions. A circumstance has to be considered in different views. For example, when a customer buys a product, not only the priority of purchasing, but variables such as region, time, climate and customer category should be also taken into account. A more applicable sequential pattern to the real situation can therefore be mined.
In this paper, we embedded FPAM as the algorithm for sequential patterns mining. Furthermore, we applied Rough Set Theory for multi-dimensional analysis. After a construction of Rough Set index structure, sequential and multi-dimensional patterns are combined to obtain multi-dimensional sequential patterns. With this kind of approach, when given frequent patterns, we can enhance the efficiency of data mining by rescanning the database only once.
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