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A novel unsupervised fuzzy clustering method for preprocessing of quantitative attributes in association rule mining

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Abstract

In Association rule mining, the quantitative attribute values are converted into Boolean values using fixed intervals. Conventional association rule mining algorithms are then applied to find relations among the attribute values. These intervals may not be concise and meaningful enough for human users to easily obtain non trivial knowledge from those rules discovered. Clustering techniques can be used for segmenting quantitative values into meaningful groups instead of fixed intervals. But the conventional clustering techniques like k-means and c-means require the user to specify the number of clusters and initial cluster centres. This initialization is one of the major challenges of clustering. A novel fuzzy based unsupervised clustering algorithm proposed by the authors is extended to segment quantitative values into fuzzy clusters in this paper. Membership values of quantitative items in the partitioning fuzzy clusters are used with weighted fuzzy rule mining techniques to find natural association rules. This fuzzy based method for handling quantitative attributes is compared with that of fixed intervals and segmenting using conventional k-means clustering method along with Apriori algorithm.

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Authors and Affiliations

  1. Department of Computer Applications, Marian College, Kuttiikkanam, Kerala, India

    Binu Thomas

  2. Department of Information Technology, Kannur University, Kannur, Kerala, India

    G. Raju

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  1. Binu Thomas
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  2. G. Raju
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Correspondence to Binu Thomas.

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Thomas, B., Raju, G. A novel unsupervised fuzzy clustering method for preprocessing of quantitative attributes in association rule mining. Inf Technol Manag 15, 9–17 (2014). https://doi.org/10.1007/s10799-013-0168-7

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