Seeding-the task of finding initial cluster centers-is critical in obtaining high-quality
clusterings for k-Means. However, k-means++ seeding, the state of the art algorithm, does …

The quality of K-Means clustering is extremely sensitive to proper initialization. The classic
remedy is to apply k-means++ to obtain an initial set of centers that is provably competitive …

Abstract The $ k $-means++ algorithm of Arthur and Vassilvitskii (SODA 2007) is often the
practitioners' choice algorithm for optimizing the popular $ k $-means clustering objective …

Genetic Algorithm with Fast Greedy Heuristic for Clustering and Location Problems 1
Introduction Page 1 Informatica 38 (2014) 229–240 229 Genetic Algorithm with Fast Greedy …

This paper studies the $ k $-means++ algorithm for clustering as well as the class of $ D^\ell
$ sampling algorithms to which $ k $-means++ belongs. It is shown that for any constant …

The classical center based clustering problems such as k-means/median/center assume that
the optimal clusters satisfy the locality property that the points in the same cluster are close …

In this paper, we consider a class of constrained clustering problems of points in R^ d R d,
where d could be rather high. A common feature of these problems is that their optimal …

Clustering is one of the most important tools for analysis of large datasets, and perhaps the
most popular clustering algorithm is Lloyd's iteration for $ k $-means. This iteration takes $ N …

The k-Means++ algorithm is the state of the art algorithm to solve k-Means clustering
problems as the computed clusterings are O (log k) competitive in expectation. However, its …

Ashtiani et al. proposed a Semi-Supervised Active Clustering framework (SSAC), where the
learner is allowed to make adaptive queries to a domain expert. The queries are of the kind" …