@article{(Open Science Index):https://publications.waset.org/pdf/6000,
	  title     = {Enhanced Clustering Analysis and Visualization Using Kohonen's Self-Organizing Feature Map Networks},
	  author    = {Kasthurirangan Gopalakrishnan and  Siddhartha Khaitan and  Anshu Manik},
	  country	= {},
	  institution	= {},
	  abstract     = {Cluster analysis is the name given to a diverse collection of techniques that can be used to classify objects (e.g. individuals, quadrats, species etc). While Kohonen's Self-Organizing Feature Map (SOFM) or Self-Organizing Map (SOM) networks have been successfully applied as a classification tool to various problem domains, including speech recognition, image data compression, image or character recognition, robot control and medical diagnosis, its potential as a robust substitute for clustering analysis remains relatively unresearched. SOM networks combine competitive learning with dimensionality reduction by smoothing the clusters with respect to an a priori grid and provide a powerful tool for data visualization. In this paper, SOM is used for creating a toroidal mapping of two-dimensional lattice to perform cluster analysis on results of a chemical analysis of wines produced in the same region in Italy but derived from three different cultivators, referred to as the “wine recognition data" located in the University of California-Irvine database. The results are encouraging and it is believed that SOM would make an appealing and powerful decision-support system tool for clustering tasks and for data visualization.
},
	    journal   = {International Journal of Civil and Environmental Engineering},
	  volume    = {2},
	  number    = {6},
	  year      = {2008},
	  pages     = {128 - 135},
	  ee        = {https://publications.waset.org/pdf/6000},
	  url   	= {https://publications.waset.org/vol/18},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 18, 2008},
	}