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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.
Dr. Ritu yadav. 1970. \u201cAdvanced Methods to Improve Performance of K-Means Algorithm: A Review\u201d. Unknown Journal GJCST Volume 12 (GJCST Volume 12 Issue 9): .
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Total Score: 107
Country: India
Subject: Uncategorized
Authors: Dr. Ritu yadav, Anuradha Sharma (PhD/Dr. count: 1)
View Count (all-time): 137
Total Views (Real + Logic): 20951
Total Downloads (simulated): 10965
Publish Date: 1970 01, Thu
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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.
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