APACS: a system for the automatic analysis and classification of conceptual patterns
Computational Intelligence
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining fuzzy association rules
CIKM '97 Proceedings of the sixth international conference on Information and knowledge management
A framework for measuring changes in data characteristics
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Information-theoretic fuzzy approach to data reliability and data mining
Fuzzy Sets and Systems
Mining fuzzy association rules in a database containing relational and transactional data
Data mining and computational intelligence
Discovering the set of fundamental rule changes
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Machine Learning
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Classification with Degree of Membership: A Fuzzy Approach
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Database summarization using fuzzy ISA hierarchies
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy decision trees: issues and methods
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Mining fuzzy association rules in a bank-account database
IEEE Transactions on Fuzzy Systems
On discovery of soft associations with "most" fuzzy quantifier for item promotion applications
Information Sciences: an International Journal
ACM SIGKDD Explorations Newsletter
Estimating confidence intervals for structural differences between contrast groups with missing data
Expert Systems with Applications: An International Journal
A new method for ranking changes in customer's behavioral patterns in department stores
Proceedings of the 11th International Conference on Electronic Commerce
Fuzzy sets in database and information systems: Status and opportunities
Fuzzy Sets and Systems
A framework for discovering and analyzing changing customer segments
ICDM'07 Proceedings of the 7th industrial conference on Advances in data mining: theoretical aspects and applications
Mining dynamic association rules with comments
Knowledge and Information Systems
Discovering association rules change from large databases
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I
Mining the change of customer behavior in fuzzy time-interval sequential patterns
Applied Soft Computing
Difference detection between two contrast sets
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
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Association rule mining is concerned with the discovery of interesting association relationships hidden in databases. Existing algorithms typically assume that data characteristics are stable over time. Their main focus is therefore to mine association rules in an efficient manner. However, the world constantly changes. This makes the characteristics of real-life entities represented by the data and hence the associations hidden in the data change over time. Detecting and adapting to the changes are usually critical to the success of many business organizations. This paper presents the problem of mining changes in association rules. Given a set of database partitions, each of which contains a set of transactions collected in a specific time period, a set of association rules is discovered in each database partition. We propose to perform data mining in the discovered association rules so as to reveal the regularities governing how the rules change in different time periods. Since the nature of many real-life entities is rather fuzzy, we propose to use linguistic variables and linguistic terms to represent the changes in the discovered association rules. In particular, fuzzy decision trees are built to discover the changes in the discovered association rules. The fuzzy decision trees are then converted to a set of fuzzy rules, called fuzzy meta-rules because they are rules about rules. By doing so, the changes hidden in the data can be revealed and presented to human users in a comprehensible form. Furthermore, the discovered changes can also be used to predict any change in the future. To evaluate the performance of our approach, we make use of a set of synthetic datasets, which are database partitions collected in different time periods. A set of association rules is discovered in each dataset. Fuzzy decision trees are constructed in the discovered association rules in order to reveal the changes in these rules. The experimental results show that our approach is very promising.