# A Kalman filtering induced heuristic optimization based partitional data clustering

## TL;DR

This paper introduces HKA-K, a hybrid clustering algorithm combining Kalman filtering and K-Means, demonstrating improved performance on various datasets compared to existing methods.

## Contribution

The paper proposes an improved hybrid clustering algorithm, HKA-K, integrating Kalman filtering and K-Means for better exploration and faster convergence.

## Key findings

- HKA-K performs at least as well as other hybrid algorithms.
- HKA-K often outperforms existing clustering methods.
- Tested on UCI datasets, showing improved clustering quality.

## 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.