Extraction and optimization of fuzzy association rules using multi-objective genetic algorithm |
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Authors: | P Santhi Thilagam V S Ananthanarayana |
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Affiliation: | (1) Department of Computer Engineering, National Institute of Technology Karnataka, Srinivasanagar, Surathkal, 575025, India;(2) Department of Information Technology, National Institute of Technology Karnataka, Srinivasanagar, Surathkal, 575025, India |
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Abstract: | Association Rule Mining is one of the important data mining activities and has received substantial attention in the literature.
Association rule mining is a computationally and I/O intensive task. In this paper, we propose a solution approach for mining optimized fuzzy association rules of different orders.
We also propose an approach to define membership functions for all the continuous attributes in a database by using clustering
techniques. Although single objective genetic algorithms are used extensively, they degenerate the solution. In our approach,
extraction and optimization of fuzzy association rules are done together using multi-objective genetic algorithm by considering
the objectives such as fuzzy support, fuzzy confidence and rule length. The effectiveness of the proposed approach is tested
using computer activity dataset to analyze the performance of a multi processor system and network audit data to detect anomaly
based intrusions. Experiments show that the proposed method is efficient in many scenarios.
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Keywords: | Fuzzy association rules Multi-objective genetic algorithms Fuzzy k-means clustering |
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