Comparison of self-organizing map with K-means hierarchical clustering for bioinformatics applications
Shahapurkar, S.S.
Sundareshan, M.K.
Sort Test Technol. Dev., Intel Corp., Chandler, AZ, USA
This paper appears in: Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on Publication Date: 25-29 July 2004
Volume: 2
On page(s):
1221
- 1226 vol.2
ISSN: 1098-7576
ISBN: 0-7803-8359-1
Digital Object Identifier: 10.1109/IJCNN.2004.1380117
Current Version Published: 2005-01-17
Abstract
The self-organizing map (SOM) has emerged as one of the popular choices for clustering data; however, when it comes to point density accuracy of codebooks or reliability and interpretability of the map, the SOM leaves much to be desired. In this paper, we compare the newly developed K-means hierarchical (KMH) clustering algorithm to the SOM. We also introduce a new initialization scheme for the K-means that improves codebook placement and, propose a novel visualization scheme that combines the principal component analysis (PCA) and minimal spanning tree (MST) in an arrangement that ensures reliability of the visualization unlike the SOM. A practical application of the algorithm is demonstrated on a challenging bioinformatics problem.
Index
Terms
Available to subscribers and IEEE members.
References
Available to subscribers and IEEE members.
Citing Documents
Available to subscribers and IEEE members.
You are not
logged in.
Guests
may access Abstract records free of charge.
Cart
