  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.
