Multi-layer topology preserving mapping for K-means clustering

Authors:
Ying Wu;Thomas K. Doyle;Colin Fyfe
Affiliations:
Coastal and Marine Research Centre, ERI, University College Cork, Glucksman Marine Facility, Naval Base, Haulbowline, Ireland;Coastal and Marine Research Centre, ERI, University College Cork, Glucksman Marine Facility, Naval Base, Haulbowline, Ireland;Applied Computational Intelligence Research Unit, The University of the West of Scotland, Scotland
Venue:
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Year:
2011

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Abstract

In this paper, we investigate the multi-layer topology preserving mapping for K-means. We present a Multi-layer Topology Preserving Mapping (MTPM) based on the idea of deep architectures. We demonstrate that the MTPM output can be used to discover the number of clusters for K-means and initialize the prototypes of K-means more reasonably. Also, K-means clusters the data based on the discovered underlying structure of the data by the MTPM. The standard wine data set is used to test our algorithm. We finally analyse a real biological data set with no prior clustering information available.