A k-means based co-clustering (kCC) algorithm for sparse, high dimensional data

作者：

Highlights：

• A probabilistic random walk model for the 3 steps of the k-means algorithm.

• Mathematical foundation for efficacy and proofs for convergence is given.

• Clustering/co-clustering results show robustness, convergence and high accuracy.

摘要

•A probabilistic random walk model for the 3 steps of the k-means algorithm.•Mathematical foundation for efficacy and proofs for convergence is given.•Clustering/co-clustering results show robustness, convergence and high accuracy.

论文关键词：Clustering,K-means,Centroid initialization,Co-clustering,Semantic similarity

论文评审过程：Received 15 July 2018, Revised 30 August 2018, Accepted 3 September 2018, Available online 5 September 2018, Version of Record 8 November 2018.

论文官网地址：https://doi.org/10.1016/j.eswa.2018.09.006