Publication Type:

Journal Article


Advances in Intelligent Systems and Computing, Springer Verlag, Volume 408, p.523-530 (2016)





cluster analysis, Clustering, Clustering algorithms, Data mining, FPMC, Normalization, Pattern analysis, planning, Sum of squared errors, Sustainable development


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.


cited By 0; Conference of International Conference on Information and Communication Technology for Sustainable Development, ICT4SD 2015 ; Conference Date: 3 July 2015 Through 4 July 2015; Conference Code:164539

Cite this Research Publication

A. Asok, Jisha, T. J., S. Ashok, and 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.