| Summary: | 碩士 === 大同大學 === 資訊經營學系(所) === 93 === In this study, we have studied the problem of mining partial periodicity in time series database. Most of the previous studies adopt an Apriori-property to mining periodic patterns. It is based on an Apriori heuristic [18]. However, the time cost of candidate set generation is expensive. On the other hand, using a single minimum support can not reflect the real-life situation.
For this reason, we propose a periodicity tree (PFP-tree for short) structure. It is design by modifying the FP-tree structure. Moreover, we also developed an efficient algorithm to mining periodic patterns with multiple minimum supports. On the other hand, we demonstrate the usefulness of these techniques through an extensive experimental study. Our research can be applied to stock market price movement, natural calamities prediction (e.g., earthquake) and a person’s shopping habit, etc. For example, in a database maintained by online shopping system, we can get periodic information of customer's shopping habit through login time and products of searching for each user. Their periodicities may reveal interesting information that can be used for prediction and decision making.
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