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Self-organizing Map with Modified Self Organizing Map Clustering

Kamalpreet Kaur Jassar1 , Kanwalvir Singh Dhindsa2

Section:Research Paper, Product Type: Journal Paper
Volume-3 , Issue-7 , Page no. 115-119, Jul-2015

Online published on Jul 30, 2015

Copyright © Kamalpreet Kaur Jassar , Kanwalvir Singh Dhindsa . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Kamalpreet Kaur Jassar , Kanwalvir Singh Dhindsa, “Self-organizing Map with Modified Self Organizing Map Clustering,” International Journal of Computer Sciences and Engineering, Vol.3, Issue.7, pp.115-119, 2015.

MLA Style Citation: Kamalpreet Kaur Jassar , Kanwalvir Singh Dhindsa "Self-organizing Map with Modified Self Organizing Map Clustering." International Journal of Computer Sciences and Engineering 3.7 (2015): 115-119.

APA Style Citation: Kamalpreet Kaur Jassar , Kanwalvir Singh Dhindsa, (2015). Self-organizing Map with Modified Self Organizing Map Clustering. International Journal of Computer Sciences and Engineering, 3(7), 115-119.

BibTex Style Citation:
@article{Jassar_2015,
author = {Kamalpreet Kaur Jassar , Kanwalvir Singh Dhindsa},
title = {Self-organizing Map with Modified Self Organizing Map Clustering},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {7 2015},
volume = {3},
Issue = {7},
month = {7},
year = {2015},
issn = {2347-2693},
pages = {115-119},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=585},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=585
TI - Self-organizing Map with Modified Self Organizing Map Clustering
T2 - International Journal of Computer Sciences and Engineering
AU - Kamalpreet Kaur Jassar , Kanwalvir Singh Dhindsa
PY - 2015
DA - 2015/07/30
PB - IJCSE, Indore, INDIA
SP - 115-119
IS - 7
VL - 3
SN - 2347-2693
ER -

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Abstract

Clustering is a very well known technique of data mining which is mostly used method of analyzing and describing the data. It is one of the techniques to deal with the large geographical datasets. Clustering is the mostly used method of data mining. Kohonen SOM is a classical method for clustering. In this paper, a new approach is proposed by combining neural network and clustering algorithms. We propose a modified Self Organizing Map algorithm which initially starts with null network and grows with the original data space as initial weight vector, updating neighbourhood rules and learning rate dynamically in order to overcome the fixed architecture and random weight vector assignment of simple SOM. In this paper, existing SOM and modified SOM have been compared by using different parameters.

Key-Words / Index Term

Clustering Algorithms, Learning rate, Weight vector, SOM, Modified SOM

References

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