You are here

A Kalman filtering induced heuristic optimization based partitional data clustering

Authors: 

Arjun Pakrashi, Bidyut B. Chaudhuri

Publication Type: 
Refereed Original Article
Abstract: 
Clustering algorithms have regained momentum with recent popularity of data mining and knowledge discovery approaches. To obtain good clustering in reasonable amount of time, various meta-heuristic approaches and their hybridization, sometimes with K-Means technique, have been employed. A Kalman Filtering based heuristic approach called Heuristic Kalman Algorithm (HKA) has been proposed a few years ago, which may be used for optimizing an objective function in data/feature space. In this paper at first HKA is employed in partitional data clustering. Then an improved approach named HKA-K is proposed, which combines the benefits of global exploration of HKA and the fast convergence of K-Means method. Implemented and tested on several datasets from UCI machine learning repository, the results obtained by HKA-K were compared with other hybrid meta-heuristic clustering approaches. It is shown that HKA-K is atleast as good as and often better than the other compared algorithms.
Digital Object Identifer (DOI): 
10.1016/j.ins.2016.07.057
Publication Status: 
Published
Date Accepted for Publication: 
Thursday, 21 July, 2016
Publication Date: 
25/07/2016
Journal: 
Information Sciences
Institution: 
National University of Ireland, Dublin (UCD)
Open access repository: 
No