The Periodical Intervals Analysis on Sequential Patterns

• Main Authors: Yi-Tian Lee, 李宜靝
• Other Authors: 蔣定安
• Format: Others
• Language: zh-TW
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/21969389403664746841

碩士 === 淡江大學 === 資訊工程學系碩士班 === 93 === In processing huge transaction data analysis, we often use Association Rules Mining and Sequential Patterns Mining techniques to discover the buying behaviors of customers. However, by sequential patterns, we are hard to find out the time intervals of related items purchased. In this paper, we develop a set of algorithms to analysis the periodical properties of time intervals over sequential patterns. The first, we introduce PDT/PDM algorithms to discover periodical distributions for common cases. Then, we extend them as LPDT/LPDM algorithms to overcome linearly trend components of curves. Finally, we combine those algorithms and sequential patterns’ distribution property as PIM (Periodical Intervals Mining) algorithm. By experiment, we use PIM algorithm to analysis the periodical distributions and use them to point out the best choice of products from sequential patterns by compare the periodical intervals.