A novel approach for data stream clustering using artificial bee colony algorithm
by Chong-Huan Xu
International Journal of Wireless and Mobile Computing (IJWMC), Vol. 8, No. 1, 2015

Abstract: This paper presents a novel approach to effectively clustering a large amount of data stream produced by some applications such as large-scale surveillance, network packet inspection and stock market. Owing to the massiveness and forgotten characteristics of the data stream, the proposed approach uses a damped window model to partition them. Then it adopts modified K-means based on the Artificial Bee Colony (ABC) algorithm to cluster this data stream fragment and dynamically update the clustering result. Detailed simulation analysis demonstrates that this algorithm is of high efficiency of space and time and is more stable.

Online publication date: Sun, 04-Jan-2015

The full text of this article 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 Wireless and Mobile Computing (IJWMC):
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