A Kalman filtering induced heuristic optimization based partitional data clustering
This is an incremental improvement for data mining practitioners seeking efficient clustering algorithms.
The paper tackles partitional data clustering by proposing HKA-K, a hybrid method combining the Heuristic Kalman Algorithm (HKA) with K-Means, and shows it performs at least as well as or better than other hybrid meta-heuristic approaches on UCI datasets.
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.