Paper Info
Title
A graph partitioning approach to SOM clustering
Abstract
Determining the number of clusters has been one of the most difficult problems in data clustering. The Self-Organizing Map (SOM) has been widely used for data visualization and clustering. The SOM can reduce the complexity in terms of computation and noise of input patterns. However, post processing steps are needed to extract the real data structure learnt by the map. One approach is to use other algorithm, such as K-means, to cluster neurons. Finding the best value of K can be aided by using an cluster validity index. On the other hand, graph-based clustering has been used for cluster analysis. This paper addresses an alternative methodology using graph theory for SOM clustering. The Davies-Bouldin index is used as a cluster validity to analyze inconsistent neighboring relations between neurons. The result is a segmented map, which indicates the number of clusters as well as the labeled neurons. This approach is compared with the traditional approach using K-means. The experimental results using the approach addressed here with three different databases presented consistent results of the expected number of clusters.
Year
DOI
Venue
2011
10.1007/978-3-642-23878-9_19
IDEAL
Keywords
Field
DocType
traditional approach,expected number,som clustering,real data structure learnt,data visualization,cluster validity index,cluster validity,cluster neuron,graph-based clustering,cluster analysis,self organizing map,graph theory,data clustering,k means
Data mining,Fuzzy clustering,Computer science,Artificial intelligence,Cluster analysis,Single-linkage clustering,k-medians clustering,Complete-linkage clustering,Correlation clustering,Pattern recognition,Determining the number of clusters in a data set,Constrained clustering,Machine learning
Conference
Volume
ISSN
Citations 
6936
0302-9743
0
PageRank 
References 
Authors
0.34
8
2
Name
Order
Citations
PageRank
Leandro A. Silva121.76
José Alfredo F. Costa25210.11