Abstract
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.
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Acknowledgments
This work is supported by the DST Funded Project, (SR/CSI/81/2011) under Cognitive Science Research Initiative in the Department of Computer Science, Amrita School of Arts and Sciences, Amrita Vishwa Vidyapeetham University, Kochi.
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Asok, A., Jisha, T.J., Ashok, S., Judy, M.V. (2016). Integrated Framework Using Frequent Pattern for Clustering Numeric and Nominal Data Sets. In: Satapathy, S., Joshi, A., Modi, N., Pathak, N. (eds) Proceedings of International Conference on ICT for Sustainable Development. Advances in Intelligent Systems and Computing, vol 408. Springer, Singapore. https://doi.org/10.1007/978-981-10-0129-1_55
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DOI: https://doi.org/10.1007/978-981-10-0129-1_55
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