NDLI: A two-stage SOM for noise reduction in SOM map

Content Provider	IEEE Xplore Digital Library
Author	Apinantanakon, W. Sunat, K.
Copyright Year	2014
Description	Author affiliation: Khon Kaen Univ., Khon Kaen, Thailand (Apinantanakon, W.; Sunat, K.)
Abstract	A Self-organizing map (SOM) is a neural network model for clustering high-dimensional data into a low-dimensional space that could be used for applications of data clustering and visualization. One of the major problems of the SOM algorithm is the difficulty for non-expert users to interpret information from a variety of clusters in a map of SOM especially, when the data set consists of noise. Noise is a sample data which has a value different from the other members and distributed in the general cluster of SOM map. In this paper, we propose two stages SOM for investigating into this problem by removing the abnormal data - noise - from each cluster. The two stages are : to calculate the cluster and the members of cluster by using the training step of standard SOM; and to calculate the variance distance of the members in each cluster and find the noise of data by retaining stage of SOM without updating the weights but to comparing the data by using variance. The experiment shows the proposed method that can detect the noise and remove the abnormal data from each cluster. Furthermore, it reveals that, with a new chunk of the data set, the results from this experiment came out in good visualization. The SOM map also became more smooth than the former. The results of the proposed methods are compared and discussed on five medical data sets.
Starting Page	198
Ending Page	202
File Size	992973
Page Count	5
File Format	PDF
e-ISBN	9781479949632
DOI	10.1109/ICSEC.2014.6978194
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-30
Publisher Place	Thailand
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Self-Organizing map Noise Neural networks Data visualization Clustering algorithms Diabetes Neural network Clustering Standards
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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