Advanced Methods to Improve Performance of  K-Means Algorithm: A Review

Advanced Methods to Improve Performance of K-Means Algorithm: A Review

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5HOM4

,

Advanced Methods to Improve Performance of K-Means Algorithm: A Review

Dr. Ritu yadav Guru Jambheshwar University of Science & Technology,Hissar,Haryana India.

Anuradha Sharma

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Abstract

Clustering is an unsupervised classification that is the partitioning of a data set in a set of meaningful subsets. Each object in dataset shares some common property- often proximity according to some defined distance measure. Among various types of clustering techniques, K-Means is one of the most popular algorithms. The objective of K-means algorithm is to make the distances of objects in the same cluster as small as possible. Algorithms, systems and frameworks that address clustering challenges have been more elaborated over the past years. In this review paper, we present the K-Means algorithm and its improved techniques.

Advanced Methods to Improve Performance of K-Means Algorithm: A Review

Clustering is an unsupervised classification that is the partitioning of a data set in a set of meaningful subsets. Each object in dataset shares some common property- often proximity according to some defined distance measure. Among various types of clustering techniques, K-Means is one of the most popular algorithms. The objective of K-means algorithm is to make the distances of objects in the same cluster as small as possible. Algorithms, systems and frameworks that address clustering challenges have been more elaborated over the past years. In this review paper, we present the K-Means algorithm and its improved techniques.

Authors

Dr. Ritu yadav Guru Jambheshwar University of Science & Technology,Hissar,Haryana India.

Anuradha Sharma

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Dr. Ritu yadav. 1970. “. Unknown Journal GJCST Volume 12 (GJCST Volume 12 Issue 9): .

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GJCST Volume 12 Issue 9
Pg. 41- 45

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Submission Received April 6, 2012
Accepted May 3, 2012
Published May 15, 2012

Keywords

Classification Clustering K-Means Clustering partitioning clustering

Article Matrices

Total Score: 107

Country: India

Subject: Uncategorized

Authors: Dr. Ritu yadav, Anuradha Sharma (PhD/Dr. count: 1)

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Publish Date: 1970 01, Thu

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