Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
A new framework for itemset generation
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Knowledge Discovery in Databases
Knowledge Discovery in Databases
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Decision Tables: Scalable Classification Exploring RDBMS Capabilities
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
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In this paper, a new effective method is proposed to find class association rules (CAR), to get useful class association rules (UCAR) by removing the spurious class association rules (SCAR), and to generate exception class association rules (ECAR) for each UCAR. CAR mining, which integrates the techniques of classification and association, is of great interest recently. However. it has two drawbacks: one is that a large part of CARs are spurious and maybe misleading to users; the other is that some important ECARs are difficult to find using traditional data mining techniques. The method introduced in this paper aims to get over these flaws. According to our approach, a user can retrieve correct information from UCARs and know the influence from different conditions by checking corresponding ECARs. Experimental results demonstrate the effectiveness of our proposed approach.