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학술지 Analyzing Sequential Patterns in Retail Databases
Cited 8 time in scopus Download 0 time Share share facebook twitter linkedin kakaostory
저자
윤은일
발행일
200703
출처
Journal of Computer Science and Technology, v.22 no.2, pp.287-296
ISSN
1000-9000
출판사
Springer
DOI
https://dx.doi.org/10.1007/s11390-007-9036-4
협약과제
07MD2500, VDMS(Vehicle & Driver Management System) 기술 개발, 김현숙
초록
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. © Science Press, Beijing, China and Springer Science + Business Media, LLC, USA 2007.
KSP 제안 키워드
Anti-monotone, Confidence measure, Data analysis, Data mining(DM), Minimum Support, Pattern mining algorithm, Sequential patterns, Weighted support, objective measure, performance study