A feature weighted affinity propagation clustering algorithm based on rough entropy reduction Online publication date: Thu, 14-Mar-2019
by Li Xu
International Journal of Collaborative Intelligence (IJCI), Vol. 2, No. 1, 2019
Abstract: In the clustering task, each feature of data sample is not taken the same contribution, some features provides more related information to the final results, if they are treated equally with other features, not only the complexity of the algorithm is increased but also the accuracy of the final results will be affected. So as a key phase in clustering, feature weighting is becoming more and more concerned by scholars. This paper proposes a feature weighted affinity propagation clustering algorithm based on rough entropy reduction (FWRER-AP). Rough entropy is used to assign weights for every feature according to their different contribution. Then the optimisation samples are used in AP clustering algorithm, we can get the final clustering results through iterations. Compared with traditional AP clustering algorithm, experiment shows that the optimal algorithm not only reduces the complexity, but also improves the accuracy at the same time.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
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 Collaborative Intelligence (IJCI):
Login with your Inderscience username and 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
Our Blog 
Follow us on Twitter 
Visit us on Facebook