The full text of this article

 

A new approach for accurate distributed cluster analysis for Big Data: competitive K-Means
by Rui Máximo Esteves; Thomas Hacker; Chunming Rong
International Journal of Big Data Intelligence (IJBDI), Vol. 1, No. 1/2, 2014

 

Abstract: The tremendous growth in data volumes has created a need for new tools and algorithms to quickly analyse large datasets. Cluster analysis techniques, such as K-Means can be distributed across several machines. The accuracy of K-Means depends on the selection of seed centroids during initialisation. K-Means++ improves on the K-Means seeder, but suffers from problems when it is applied to large datasets. In this paper, we describe a new algorithm and a MapReduce implementation we developed that addresses these problems. We compared the performance with three existing algorithms and found that our algorithm improves cluster analysis accuracy and decreases variance. Our results show that our new algorithm produced a speedup of 76 ± 9 times compared with the serial K-Means++ and is as fast as the streaming K-Means. Our work provides a method to select a good initial seeding in less time, facilitating fast accurate cluster analysis over large datasets.

Online publication date: Wed, 23-Jul-2014

 

is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

 
Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

 
Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Big Data Intelligence (IJBDI):
Login with your Inderscience username and password:

 

    Username:        Password:         

Forgotten your password?


 
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

 
If you still need assistance, please email subs@inderscience.com