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A Comparative Study on Self-Organization Map Clustering Method using Breast Cancer Dataset


R. Prabu , Muthayammal College of Arts & Science; M. Sudha, Muthayammal College of Arts & Science


Clustering, Breast Cancer Dataset


Artificial neural networks (ANNs) are computational models inspired by an animal's central nervous systems (in particular the brain), and are used to estimate or approximate functions that can depend on a large number of inputs and are generally unknown. Artificial neural networks are generally presented as systems of interconnected "neurons" which can compute values from inputs, and are capable of machine learning as well as pattern recognition thanks to their adaptive nature. Clustering is a technique to group together a set of items having similar characteristics. In the clustering process can classified in to different types. In those types, partitioning clustering is the one of the clustering methods. In this thesis, an attempt is made to develop an neural network based clustering algorithm in partitioning clustering method for yeast database, The algorithm works faster so and compared with the traditional k means clustering algorithm and tested the performance of the different clustering algorithm with different cluster centroid values and also finding the optimal cluster center to improve the clustering process. The experimental results shows that the enhanced neural networking based clustering algorithm perform well and comparatively better than the traditional k means clustering algorithm for clustering yeast databases.

Other Details

Paper ID: IJSRDV4I90412
Published in: Volume : 4, Issue : 9
Publication Date: 01/12/2016
Page(s): 681-686

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