@article{(Open Science Index):https://publications.waset.org/pdf/4313,
	  title     = {Performance Comparison of Particle Swarm Optimization with Traditional Clustering Algorithms used in Self-Organizing Map},
	  author    = {Anurag Sharma and  Christian W. Omlin},
	  country	= {},
	  institution	= {},
	  abstract     = {Self-organizing map (SOM) is a well known data
reduction technique used in data mining. It can reveal structure in
data sets through data visualization that is otherwise hard to detect
from raw data alone. However, interpretation through visual
inspection is prone to errors and can be very tedious. There are
several techniques for the automatic detection of clusters of code
vectors found by SOM, but they generally do not take into account
the distribution of code vectors; this may lead to unsatisfactory
clustering and poor definition of cluster boundaries, particularly
where the density of data points is low. In this paper, we propose the
use of an adaptive heuristic particle swarm optimization (PSO)
algorithm for finding cluster boundaries directly from the code
vectors obtained from SOM. The application of our method to
several standard data sets demonstrates its feasibility. PSO algorithm
utilizes a so-called U-matrix of SOM to determine cluster boundaries;
the results of this novel automatic method compare very favorably to
boundary detection through traditional algorithms namely k-means
and hierarchical based approach which are normally used to interpret
the output of SOM.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {3},
	  number    = {3},
	  year      = {2009},
	  pages     = {642 - 653},
	  ee        = {https://publications.waset.org/pdf/4313},
	  url   	= {https://publications.waset.org/vol/27},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 27, 2009},
	}