An efficient Self-organizing map learning algorithm with winning frequency of neurons for clustering application

Author

Chaudhary, Varun ; Ahlawat, A.K. ; Bhatia, R.S.

Author_Institution

Nat. Inst. of Technol. (N.I.T.), Kurukshetra, India

fYear

2013

fDate

22-23 Feb. 2013

Firstpage

672

Lastpage

676

Abstract

The Self-organizing map (SOM) has been extensively applied to data clustering, image analysis, dimension reduction, and so forth. The conventional SOM does not calculate the winning frequency of each neuron. In this study, we propose a modified SOM which calculate the winning frequency of each neuron. We investigate the behavior of modified SOM in detail. The learning performance is evaluated using the three measurements. We apply modified SOM to various input data set and confirm that modified SOM obtain a more effective map reflecting the distribution state of the input data.

Keywords

pattern clustering; performance evaluation; self-organising feature maps; unsupervised learning; clustering application; data clustering; dimension reduction; image analysis; learning performance evaluation; modified SOM; neurons; self-organizing map learning algorithm; unsupervised neural network; winning frequency; Conferences; Mathematical model; Neurons; Quantization (signal); Self-organizing feature maps; Topology; Vectors; Self-organizing map (SOM); modified SOM; winning frequency;

fLanguage

English

Publisher

ieee

Conference_Titel

Advance Computing Conference (IACC), 2013 IEEE 3rd International

Conference_Location

Ghaziabad

Print_ISBN

978-1-4673-4527-9

Type

conf

DOI

10.1109/IAdCC.2013.6514307

Filename

6514307