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General Information
    • ISSN: 2010-3700
    • Abbreviated Title: Int. J. Mach. Learn. Comput.
    • DOI: 10.18178/IJMLC
    • Editor-in-Chief: Dr. Lin Huang
    • Executive Editor:  Ms. Cherry L. Chen
    • Abstracing/Indexing: Scopus(since 2017), EI (INSPEC, IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
    • E-mail: ijmlc@ejournal.net
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2012 Vol.2(3): 333-338 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.140

SPPS: Supervised Projected Clustering Method Based on Particle Swarm Optimization

Satish Gajawada and Durga Toshniwal

Abstract—Supervised clustering algorithms are applied on classified examples with the goal of determining class-uniform clusters. These methods evaluate clustering solutions based on class impurity unlike traditional clustering methods. These methods can be used for tasks like data editing and learning of subclasses to enhance classification methods. Supervised clustering methods have been proposed in literature to find class-uniform full dimensional clusters. But for high dimensional dataset with subspace clusters there is need for supervised clustering method which finds class-uniform subspace clusters. In this paper we propose Supervised Projected clustering Particle Swarm optimization method (SPPS method). The proposed method has been applied on Wisconsin breast cancer data to find subspace clusters present in this dataset. The SPPS method may be used for pre-processing of high dimensional datasets with subspace clusters.

Index Terms—Supervised clustering, projected clustering, particle swarm optimization, pre-processing.

Authors are with the Indian Institute of Technology Roorkee, Roorkee, India (email: gajawadasatish@gmail.com; durgafec@iitr.ernet.in).


Cite: Satish Gajawada and Durga Toshniwal, "SPPS: Supervised Projected Clustering Method Based on Particle Swarm Optimization," International Journal of Machine Learning and Computing vol. 2, no. 3, pp. 333-338, 2012.

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