Articles liés à An Improved K-means Clustering Algorithm For Data Mining

An Improved K-means Clustering Algorithm For Data Mining - Couverture souple

Aggarwal, Neha; Aggarwal, Kirti

 
9783659216657: An Improved K-means Clustering Algorithm For Data Mining
Couverture souple
ISBN 10 : 3659216658 ISBN 13 : 9783659216657
Editeur : LAP LAMBERT Academic Publishing, 2012

Synopsis

Data clustering is an unsupervised classification method aims at creating groups of objects, or clusters, in such a way that objects in the same cluster are very similar and objects in different clusters are quite distinct. K-means is an iterative algorithm in which the number of clusters must be determined before the execution.In this book an efficient k-means algorithm is proposed. Since, in each iteration, the k-means algorithm computes the distances between data point and all centers, this is computationally very expensive especially for huge data sets. For each data point, we can keep the distance to the nearest cluster. At the next iteration, we compute the distance to the previous nearest cluster. If the new distance is less than or equal to the previous distance, the point stays in its cluster, and there is no need to compute its distances to the other cluster centers. This saves the time required to compute distances to k−1 clusters. Experimental results show the accuracy and effectiveness of the proposed method.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

Data clustering is an unsupervised classification method aims at creating groups of objects, or clusters, in such a way that objects in the same cluster are very similar and objects in different clusters are quite distinct. K-means is an iterative algorithm in which the number of clusters must be determined before the execution.In this book an efficient k-means algorithm is proposed. Since, in each iteration, the k-means algorithm computes the distances between data point and all centers, this is computationally very expensive especially for huge data sets. For each data point, we can keep the distance to the nearest cluster. At the next iteration, we compute the distance to the previous nearest cluster. If the new distance is less than or equal to the previous distance, the point stays in its cluster, and there is no need to compute its distances to the other cluster centers. This saves the time required to compute distances to k−1 clusters. Experimental results show the accuracy and effectiveness of the proposed method.

Biographie de l'auteur

Neha Aggarwal is a computer professional working as an Assistant Professor in MDU, India. she is B.Tech(CSE), MBA(HR), M.Tech(CSE) with about 8 International journals in her name.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2012
Langue
anglais
ISBN 10 
3659216658
ISBN 13 
9783659216657
Reliure
Broché
Nombre de pages
72
Coordonnées du fabricant
non disponible
Personne responsable
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