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Comparative Study and Performance Analysis of Clustering Algorithms

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IJCA Proceedings on International Conference on ICT for Healthcare
© 2016 by IJCA Journal
ICTHC 2015 - Number 1
Year of Publication: 2016
Authors:
Kamalpreet Kaur Jassar
Kanwalvir Singh Dhindsa

Kamalpreet Kaur Jassar and Kanwalvir Singh Dhindsa. Article: Comparative Study and Performance Analysis of Clustering Algorithms. IJCA Proceedings on International Conference on ICT for Healthcare ICTHC 2015(1):1-6, April 2016. Full text available. BibTeX

@article{key:article,
	author = {Kamalpreet Kaur Jassar and Kanwalvir Singh Dhindsa},
	title = {Article: Comparative Study and Performance Analysis of Clustering Algorithms},
	journal = {IJCA Proceedings on International Conference on ICT for Healthcare},
	year = {2016},
	volume = {ICTHC 2015},
	number = {1},
	pages = {1-6},
	month = {April},
	note = {Full text available}
}

Abstract

Spatial clustering is a process of grouping a set of spatial objects into groups, these groups are called clusters. Objects within a one cluster show a high degree of similarity, whereas the objects in another cluster are as much non-similar as possible. 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. SOM and k-means are two classical methods for clustering. This paper illustrates the approach of clustering: Kohonen SOM and K-Means have been discussed and compared using different parameters on same dataset. After comparing these methods effectively, results of the experiments suggest that Self-Organizing Maps (SOM) is more robust to outlier than the k-means method. In this paper, experiments have been performed to compare the performances of clustering algorithms.

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