Abstract
In order to solve the problem of mining quantitative association rules, an algorithm named Fuzzy Pattern Fusion based on Competitive Agglomeration (FPF-CA) is developed in this paper. The proposed algorithm is based on the superior functionalities of Fuzzy Pattern Fusion (FPF) for mining quantitative association rules and Competitive Agglomeration (CA) for finding the optimal number of clusters. The popular data set of UCI machine learning repository is used to demonstrate the feasibility of the FPF-CA algorithm. The simulation experiment results show that the proposed algorithm can efficiently mine quantitative association rules according to the actual data distribution.
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Jia, J., Lu, Y., Chu, J., Su, H. (2015). Fuzzy Clustering-Based Quantitative Association Rules Mining in Multidimensional Data Set. In: Tan, Y., Shi, Y., Buarque, F., Gelbukh, A., Das, S., Engelbrecht, A. (eds) Advances in Swarm and Computational Intelligence. ICSI 2015. Lecture Notes in Computer Science(), vol 9142. Springer, Cham. https://doi.org/10.1007/978-3-319-20469-7_9
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DOI: https://doi.org/10.1007/978-3-319-20469-7_9
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