Simulated annealing-based text clustering

Abstract

Like traditional K-means, the main drawback of spherical K-means is its high sensitivity to the initialization of centroids. This issue can cause the algorithm to converge to poor local optima, resulting in clusters that do not accurately reflect the true structure of the data. In this paper, we propose two new text clustering algorithms that are less sensitive to initialization and that significantly improve clustering performance. The first algorithm employs simulated annealing to avoid getting trapped in poor local optima. The second algorithm, a relaxed version of simulated annealing, also uses randomization to escape poor local optima but requires significantly fewer computations than the first algorithm. The two algorithms are extensively evaluated across more than thirty text datasets. Experimental results demonstrate that the proposed approaches significantly outperform well-established text clustering algorithms in terms of clustering quality. Furthermore, the second algorithm is as efficient as standard spherical K-means regarding clustering speed, as both have the same time complexity. Finally, an important advantage of the proposed algorithms is that they can be applied to other domains involving directional data, such as recommender systems, social network analysis, image analysis, and more.