Knowledge gathering of fuzzy multi-time-interval sequential patterns

被引:12
作者
Huang, Tony Cheng-Kui [1 ]
机构
[1] Natl Chung Cheng Univ, Dept Business Adm, Chiayi, Taiwan
关键词
Data mining; Sequence data; Sequential pattern; Multi-time-interval; Fuzzy sets; ALGORITHM; DISCOVERY; DATABASES;
D O I
10.1016/j.ins.2010.04.024
中图分类号
TP [自动化技术、计算机技术];
学科分类号
080201 [机械制造及其自动化];
摘要
Mining sequential patterns to find ordered events or subsequence patterns is essential in many applications, such as analysis of consumer shopping data, web clickstreams, and biological sequences. Traditional patterns reveal which items are frequently purchased together and in what order. However, information about the time intervals between purchases is missing. Therefore, Yang proposed using multi-time-interval sequential patterns to consider the time intervals between each pair of items in a pattern. For example, (Bread, ti(1), Millk, (ti(2), ti(1)), Jam) means that Bread is bought before Milk within an interval of ti(1), and Jam is bought after Bread and Milk within intervals of ti(2) and ti(1), respectively, where ti(1) and ti(2) are predefined time intervals. Although this new type of pattern considers the intervals between all pairs of items, it contains a sharp boundary problem; that is, when the time interval between two purchases is near the boundary of two predetermined time ranges, we either ignore or overemphasize it. In this study, we applied the concept of fuzzy sets to solve the sharp boundary problem. The discovered patterns, called fuzzy multi-time-interval sequential patterns, describe time intervals in linguistic terms for better understanding. Two algorithms, FuzzMI-Apriori and FuzzMI-PrefixSpan, were developed for mining fuzzy multi-time-interval patterns. Experiments using synthetic and real datasets showed the algorithms' computational efficiency, scalability, and effectiveness. (C) 2010 Elsevier Inc. All rights reserved.
引用
收藏
页码:3316 / 3334
页数:19
相关论文
共 41 条
[1]
AGRAWAL R, 1995, PROC INT CONF DATA, P3, DOI 10.1109/ICDE.1995.380415
[2]
Agrawal R., 1993, Proceedings of the International Conference on Foundations of Data Organization and Algorithms, Chicago, IL, P69
[3]
[Anonymous], 2017, Fuzzy Logic With Engineering Applications
[4]
[Anonymous], P PAC AS C KNOWL DIS
[5]
Chang BCH, 2001, JOINT 9TH IFSA WORLD CONGRESS AND 20TH NAFIPS INTERNATIONAL CONFERENCE, PROCEEDINGS, VOLS. 1-5, P1116, DOI 10.1109/NAFIPS.2001.944761
[6]
Efficient strategies for tough aggregate constraint-based sequential pattern mining [J].
Chen, Enhong ;
Cao, Huanhuan ;
Li, Qing ;
Qian, Tieyun .
INFORMATION SCIENCES, 2008, 178 (06) :1498-1518
[7]
Efficient data mining for path traversal patterns [J].
Chen, MS ;
Park, JS ;
Yu, PS .
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 1998, 10 (02) :209-221
[8]
A new approach for discovering fuzzy quantitative sequential patterns in sequence databases [J].
Chen, Yen-Liang ;
Huang, Tony Cheng-Kui .
FUZZY SETS AND SYSTEMS, 2006, 157 (12) :1641-1661
[9]
Discovering fuzzy time-interval sequential patterns in sequence databases [J].
Chen, YL ;
Huang, TCK .
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 2005, 35 (05) :959-972
[10]
Mining hybrid sequential patterns and sequential rules [J].
Chen, YL ;
Chen, SS ;
Hsu, PY .
INFORMATION SYSTEMS, 2002, 27 (05) :345-362