Clustering is an exploratory technique in data mining that aligns objects which have a maximum degree of similarity in the same group. The real-world data are usually mixed in nature, i.e., it can contain both numeric and nominal data. Performance degradation is a major challenge in existing mixed data clustering due to multiple iterations and increased complexities. We propose an integrated framework using frequent pattern analysis, frequent pattern-based framework for mixed data clustering (FPMC) algorithm, to cluster mixed data in a competent way by performing a one-time clustering along with attribute reduction. This algorithm comes under divide-and-conquer paradigm, with three phases, namely crack, transformation, and merging. The results are promising when the algorithm is applied on benchmark datasets. © Springer Science+Business Media Singapore 2016.
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A. Asok, Jisha, T. J., S. Ashok, and Dr. M.V. Judy, “Integrated framework using frequent pattern for clustering numeric and nominal data sets”, Advances in Intelligent Systems and Computing, vol. 408, pp. 523-530, 2016.