Analyzing Sequential Patterns in Retail Databases  

作　　者：Unil Yun 

机构地区：[1]Electronics and Telecommunications Research Institute, Telematics & USN Research Division LBS/Telematics Convergence Research Team, 161 Gajeong-dong, Yuseong-gu, Daejeon, 305-700, Korea

出　　处：《Journal of Computer Science & Technology》2007年第2期287-296,共10页计算机科学技术学报（英文版）

摘　　要：Finding correlated sequential patterns in large sequence databases is one of the essential tasks in data mining since a huge number of sequential patterns are usually mined, but it is hard to find sequential patterns with the correlation. According to the requirement of real applications, the needed data analysis should be different. In previous mining approaches, after mining the sequential patterns, sequential patterns with the weak affinity are found even with a high minimum support. In this paper, a new framework is suggested for mining weighted support affinity patterns in which an objective measure, sequential ws-confidence is developed to detect correlated sequential patterns with weighted support affinity patterns. To efficiently prune the weak affinity patterns, it is proved that ws-confidence measure satisfies the anti-monotone and cross weighted support properties which can be applied to eliminate sequential patterns with dissimilar weighted support levels. Based on the framework, a weighted support affinity pattern mining algorithm （WSMiner） is suggested. The performance study shows that WSMiner is efficient and scalable for mining weighted support affinity patterns.Finding correlated sequential patterns in large sequence databases is one of the essential tasks in data mining since a huge number of sequential patterns are usually mined, but it is hard to find sequential patterns with the correlation. According to the requirement of real applications, the needed data analysis should be different. In previous mining approaches, after mining the sequential patterns, sequential patterns with the weak affinity are found even with a high minimum support. In this paper, a new framework is suggested for mining weighted support affinity patterns in which an objective measure, sequential ws-confidence is developed to detect correlated sequential patterns with weighted support affinity patterns. To efficiently prune the weak affinity patterns, it is proved that ws-confidence measure satisfies the anti-monotone and cross weighted support properties which can be applied to eliminate sequential patterns with dissimilar weighted support levels. Based on the framework, a weighted support affinity pattern mining algorithm （WSMiner） is suggested. The performance study shows that WSMiner is efficient and scalable for mining weighted support affinity patterns.

关 键 词：data mining sequential pattern mining sequential ws-confidence weighted support affinity 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]