  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.
