IMPROVING THE CLUSTERING ALGORITHM K -MEA NS USING A NEW DISTANCE FUNCTION AND ITS APPLICATION TO THE POPULATION DATABASES OF BREAST CANCER

Niño Elias D., Nieto Wilson, Bolivar Juan. Improving The Clustering Algorithm K -Mea Ns Using A New Distance Function And Its Application To The Population Databases Of Breast Cancer, Proceedings of the International Conference on Computer and Computational Intelligence, ASME, ISBN: 9780791859926, Bangkok – Thailand, December 2011.

ABSTRACT

In this paper we propose an improvement to the clustering heuristic algorithm K-means. This improvement has been tested with databases of breast cancer. Today, clustering problems are everywhere; we can see its application in data mining, learning machines, knowledge discovery, data compression, pattern recognition, among others. One of the most popular and used clustering methods is the K -means , on this algorithm has been worked hard, basically have made several improvements, many of these base d on the definition of the initial parameters. In contrast, this paper proposes a ne w function to calculate the distance; this improvement comes from the experimental analysis of the classical algorithm. Experimentally, the improved algorithm showed a better quality solution being applied to population databases of breast cancer. Finally, we believe that this improvement may be useful in many types of applications, so this application can serve as a support tool for research on breast cancer and as a decision making in the allocation of resources for prevention and treatment.

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