Fuzzy taxonomy, quantitative database and mining generalized association rules

Authors:
Wang Shitong;Korris F. L. Chung;Shen Hongbin
Affiliations:
School of Information, Southern Yangtse University, WuXi, Jiangsu, China. Department of Computing, HongKong Polytechnic University, HongKong, China;School of Information, Southern Yangtse University, WuXi, Jiangsu, China. Department of Computing, HongKong Polytechnic University, HongKong, China;School of Information, Southern Yangtse University, WuXi, Jiangsu, China. Department of Computing, HongKong Polytechnic University, HongKong, China
Venue:
Intelligent Data Analysis
Year:
2005

Citing 11
Cited 0

Mining association rules between sets of items in large databases

SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Set-oriented data mining in relational databases

Data & Knowledge Engineering
Mining quantitative association rules in large relational tables

SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Data mining using two-dimensional optimized association rules: scheme, algorithms, and visualization

SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Fast discovery of association rules

Advances in knowledge discovery and data mining
Fuzzy logic in data modeling: semantics, constraints, and database design

Fuzzy logic in data modeling: semantics, constraints, and database design
A statistical theory for quantitative association rules

KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining association rules with multiple minimum supports

KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Multiple-Level Association Rules in Large Databases

IEEE Transactions on Knowledge and Data Engineering
Alternative Interest Measures for Mining Associations in Databases

IEEE Transactions on Knowledge and Data Engineering
An Efficient Algorithm for Mining Association Rules in Large Databases

VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases

Quantified Score

Hi-index	0.00

Visualization

Abstract

Mining association rules from databases is still a hot topic in data mining community in recent years. Due to the existence of multiple levels of abstraction (i.e, taxonomic structures) among the attributes of the databases, several algorithms were proposed to mine generalized Boolean association rules upon all levels of presumed crisp taxonomic structures. However, fuzzy taxonomic structures may be more suitable in many practical applications. In [9], the authors proposed an approach to mine generalized Boolean association rules with such fuzzy taxonomic structures. The main contribution of this paper is to extend their idea to mine generalized association rules from quantitative databases with fuzzy taxonomic structures. A new fuzzy taxonomic quantitative database model is presented, and the experimental results on realistic databases are demonstrated to validate this new model.