Data Mining and Knowledge Engineering

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

K-Means Algorithm for Centroid Detection and Estimation of Number of Clusters-A Review


Affiliations
1 Sri Krishna College of Engineering and Technology, Coimbatore, India
2 Department of Computer Science, Sri Krishna College of Engineering and Technology, Coimbatore, India
3 Computer Science and Engineering Department, Bannari Amman Institute of Technology, India
     

   Subscribe/Renew Journal


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.

Keywords

Classification, Clustering, K-Means Clustering, Partitioning Clustering.
User
Subscription Login to verify subscription
Notifications
Font Size

Abstract Views: 263

PDF Views: 2




  • K-Means Algorithm for Centroid Detection and Estimation of Number of Clusters-A Review

Abstract Views: 263  |  PDF Views: 2

Authors

D. Sharmila Rani
Sri Krishna College of Engineering and Technology, Coimbatore, India
N. Kousika
Department of Computer Science, Sri Krishna College of Engineering and Technology, Coimbatore, India
G. Komarasamy
Computer Science and Engineering Department, Bannari Amman Institute of Technology, India

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.

Keywords


Classification, Clustering, K-Means Clustering, Partitioning Clustering.