Mining Partial Periodic Patterns with Multiple Minimum Supports

• Main Authors: Zhe-Min Lin, 林哲民
• Other Authors: Yen-Ju Yang
• Format: Others
• Language: en_US
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/39365168291622218609

碩士 === 大同大學 === 資訊經營研究所 === 94 === 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 [18] to mining periodic patterns. However, the time cost of candidate set generation is expensive and 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 designed by modifying the FP-tree structure. Moreover, we develop an efficient algorithm to mining periodic patterns with multiple minimum supports and 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 reveal interesting information that can be used for prediction and decision making.