A new approach for discovering fuzzy quantitative sequential patterns in sequence databases-Reference-Cited by-同舟云学术

A new approach for discovering fuzzy quantitative sequential patterns in sequence databases

Published:2006-06 Issue:12 Volume:157 Page:1641-1661
ISSN:0165-0114
Container-title:Fuzzy Sets and Systems
language:en
Short-container-title:Fuzzy Sets and Systems

Author:

Chen Yen-Liang,Huang Tony Cheng-Kui

Publisher

Elsevier BV

Subject

Artificial Intelligence,Logic

Reference31 articles.

1. R. Agrawal, R. Srikant, Fast algorithms for mining association rules, in: Proc. 1994 Internat. Conf. Very Large Data Bases, 1994, pp. 487–499.

2. R. Agrawal, R. Srikant, Mining sequential patterns, in: Proc. 1995 Internat. Conf. Data Engineering, 1995, pp. 3–14.

3. W.H. Au, K.C.C. Chan, Mining fuzzy association rules, in: Proc. 6th Internat. Conf. Information Knowledge Management, Las Vegas, NV, 1997, pp. 209–215.

4. W.H. Au, K.C.C. Chan, An effective algorithm for discovering fuzzy rules in relational databases, in: Proc. IEEE Internat. Conf. Fuzzy Systems, vol. II, 1998, pp. 1314–1319.

5. W.H. Au, K.C.C. Chan, FARM: a data mining system for discovering fuzzy association rules, in: Proc. FUZZ-IEEE’99, vol. 3, 1999, pp. 22–25.

Cited by 16 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Change-Detection Machine Learning Model for Educational Management;Cybernetics and Systems;2022-05-30

2. Mining of Frequent and High Utility Fuzzy Sequential Patterns;2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC);2022-05-09

3. Mining Transactional Databases for Frequent and High-Utility Fuzzy Sequential Patterns With Time Intervals;IEEE Access;2022

4. Discovering diverse human behavior from two-dimensional preferences;Knowledge-Based Systems;2018-07

5. Knowledge discovery of consensus and conflict interval-based temporal patterns: A novel group decision approach;Knowledge-Based Systems;2018-01