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A Modified K-Means Algorithm that Determines Number of Clusters Automatically


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1 Banasthali University, Banasthali, Rajasthan, India
     

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This paper presents a modified k-means that not only increases classification accuracy but also determines optimal number of clusters automatically. It is a two phase clustering algorithm based on k-means, membership degree and standard deviation. Phase I automatically determines the number of clusters. Standard deviation of membership value helps us to identify number of clusters automatically. The phase II increases the classification accuracy. It does not require number of clusters as parameters like other previous most widely used unsupervised pattern clustering algorithms such as Fuzzy C-means and K-means. Experiments were done on some benchmark datasets and synthetic datasets to evaluate the performance of proposed approach.

Keywords

Clustering, Validitymeasures, K-Means, Membership Degree Introduction.
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  • A Modified K-Means Algorithm that Determines Number of Clusters Automatically

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Authors

Jyoti Sharma
Banasthali University, Banasthali, Rajasthan, India
G. N. Purohit
Banasthali University, Banasthali, Rajasthan, India

Abstract


This paper presents a modified k-means that not only increases classification accuracy but also determines optimal number of clusters automatically. It is a two phase clustering algorithm based on k-means, membership degree and standard deviation. Phase I automatically determines the number of clusters. Standard deviation of membership value helps us to identify number of clusters automatically. The phase II increases the classification accuracy. It does not require number of clusters as parameters like other previous most widely used unsupervised pattern clustering algorithms such as Fuzzy C-means and K-means. Experiments were done on some benchmark datasets and synthetic datasets to evaluate the performance of proposed approach.

Keywords


Clustering, Validitymeasures, K-Means, Membership Degree Introduction.

References