Performance improvements of a Kohonen self organizing classification algorithm on sparse data sets

Authors:
Francesco Maiorana
Affiliations:
University of Catania, Department of Computer Engineering and Telecomminications, Catania, Italy
Venue:
MAMECTIS'08 Proceedings of the 10th WSEAS international conference on Mathematical methods, computational techniques and intelligent systems
Year:
2008

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Abstract

This paper presents a variation of a Kohonen self organizing feature map. From the proposed algorithm possible performance improvements are investigated in terms of time and space complexity taking advantage from a sparse input data set. The proposed variation has been tested on different datasets coming from case studies in the field of bioinformatics. The improvements make the application of the algorithms feasible to massive document collections. The application of the proposed improvements for grid implementations could be beneficial to reduce the computing element demand.