… cluster structures from heterogeneous data. As an efficient approach for consensus clustering,
the K-means based … a systematic study of K-means-based consensus clustering (KCC). …

… As an efficient approach for consensus clustering, the Kmeans based method has garnered
… provide a systematic study on the framework of K-meansbased Consensus Clustering (KCC)…

… terms of consensus clustering and K-means initialization are … study, GKCC is proposed to
solve the sensitivity of K-means … K-means, and separate process of consensus clustering into a …

… a probability k-means clustering … k-means clustering algorithm to classify large number of
DMs. Furthermore, several simulation experiments between the traditional k-means clustering …

… [44] supports GPU processing for more efficient computations and it is implemented in
Python while it supports k-means, hierarchical clustering, self-organizing maps, and partition …

… [42] integrated k-means clustering algorithm and synthetic minority oversampling technique
to eliminate noisy data and to effectively obtain a balanced dataset within classes. Recently, …

… However, conventional consensus clustering methods only focus on the … for consensus
learning. In this paper, we provide an alternative idea to improve the final consensus clustering …

… purpose of appropriately deriving a consensus clustering from a clustering ensemble. The …
scheme is tested in the context of k-means based clustering, a new algorithm voting-k-means - …

… inherited from classic K-means clustering methods. However, … suitable for K-means-based
consensus clustering (KCC) is … a systematic study of K-means-based consensus clustering. …

… The input clusterings are generated by running k-Means for 2000 times over a dataset of
n objects. Further they are divided in to 100 subsets each with 20 input clusterings thus …