Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Business applications of data mining
Communications of the ACM - Evolving data mining into solutions for insights
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Forecasting Association Rules Using Existing Data Sets
IEEE Transactions on Knowledge and Data Engineering
Objective and Subjective Algorithms for Grouping Association Rules
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Post-processing of associative classification rules using closed sets
Expert Systems with Applications: An International Journal
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Frequent pattern mining and consequently association rule mining is a useful technique for discovering relationships between items in databases. However, as the size of the data to be analyzed increases or the values of the pruning thresholds decrease, larger number of frequent pattern and more association rules will be generated with little information about the association rules in relation to each other. This research paper discusses a method to segment rules into different sets with no internal conflicts. The goal is to establish an effective method to reduce the difficulty for businesses to review the association rules of different customer segments, and track the behaviors of market segments based on their buying behaviors. The method established in this paper has the advantage of not needing customer information, thus removing the need for businesses to obtain customer information. This removes the threat of intrusions into customer privacy. The method also generates the rule sets based on conflicting rules, and dividing rules based on customer behaviors is more accurate than customer characteristics. The proposed method has been validated by running some tests.