K-Means Clustering using Tabu Search with Quantized Means

TL;DR

This paper introduces a low-complexity Tabu Search method for K-Means clustering that uses quantized means and gradient-based neighborhood exploration, improving clustering quality and reducing computation time.

Contribution

It presents a novel, parameter-light TS-based K-Means algorithm with a gradient-informed neighborhood structure, enhancing efficiency and performance over existing TS methods.

Findings

Significant reduction in intra-cluster sum of squares

Lower computational time compared to existing TS approaches

Effective on real-world datasets

Abstract

The Tabu Search (TS) metaheuristic has been proposed for K-Means clustering as an alternative to Lloyd's algorithm, which for all its ease of implementation and fast runtime, has the major drawback of being trapped at local optima. While the TS approach can yield superior performance, it involves a high computational complexity. Moreover, the difficulty in parameter selection in the existing TS approach does not make it any more attractive. This paper presents an alternative, low-complexity formulation of the TS optimization procedure for K-Means clustering. This approach does not require many parameter settings. We initially constrain the centers to points in the dataset. We then aim at evolving these centers using a unique neighborhood structure that makes use of gradient information of the objective function. This results in an efficient exploration of the search space, after which the means are refined. The proposed scheme is implemented in MATLAB and tested on four real-world datasets, and it achieves a significant improvement over the existing TS approach in terms of the intra cluster sum of squares and computational time.