Mining top-k co-occurrence items with sequential pattern

doi:10.1016/j.eswa.2017.05.021

CORC > 北京大学 > 信息科学技术学院

	Mining top-k co-occurrence items with sequential pattern
	Tung Kieu ; Bay Vo ; Tuong Le ; Deng, Zhi-Hong ; Bac Le
刊名	EXPERT SYSTEMS WITH APPLICATIONS
	2017
关键词	Top-k mining Co-occurrence sequential mining Sequential pattern mining FREQUENT PATTERNS EFFICIENT ALGORITHM
DOI	10.1016/j.eswa.2017.05.021
英文摘要	Frequent sequential pattern mining has become one of the most important tasks in data mining. It has many applications, such as sequential analysis, classification, and prediction. How to generate candidates and how to control the combinatorically explosive number of intermediate subsequences are the most difficult problems. Intelligent systems such as recommender systems, expert systems, and business intelligence systems use only a few patterns, namely those that satisfy a number of defined conditions. Challenges include the mining of top-k patterns, top-rank-k patterns, closed patterns, and maximal patterns. In many cases, end users need to find itemsets that occur with a sequential pattern. Therefore, this paper proposes approaches for mining top-k co-occurrence items usually found with a sequential pattern. The Naive Approach Mining (NAM) algorithm discovers top-k co-occurrence items by directly scanning the sequence database to determine the frequency of items. The Vertical Approach Mining (VAM) algorithm is based on vertical database scanning. The Vertical with Index Approach Mining (VIAM) algorithm is based on a vertical database with index scanning. VAM and VIAM use pruning strategies to reduce the search space, thus improving performance. VAM and VIAM are especially effective in mining the co-occurrence items of a long input pattern. The three algorithms were evaluated using real-world databases. The experimental results show that these algorithms perform well, especially VAM and VIAM. (C) 2017 Elsevier Ltd. All rights reserved.; Vietnam's National Foundation for Science and Technology Development (NAFOSTED) [102.05-2015.07]; SCI(E); ARTICLE; 123-133; 85
语种	英语
内容类型	期刊论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/470446]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Tung Kieu,Bay Vo,Tuong Le,et al. Mining top-k co-occurrence items with sequential pattern[J]. EXPERT SYSTEMS WITH APPLICATIONS,2017.
APA	Tung Kieu,Bay Vo,Tuong Le,Deng, Zhi-Hong,&Bac Le.(2017).Mining top-k co-occurrence items with sequential pattern.EXPERT SYSTEMS WITH APPLICATIONS.
MLA	Tung Kieu,et al."Mining top-k co-occurrence items with sequential pattern".EXPERT SYSTEMS WITH APPLICATIONS (2017).