Regularized K-means Clustering for Multi-View Data

Clustering is a common problem in machine learning. Multi-view data can better express the integrity of data because most of the data currently processed is single-view data. Therefore, the problem of clustering analysis of multi-view data has attracted a lot of attention, and how to improve the accuracy of clustering analysis of multi-view data has become a hot topic. We propose the regularized K-means (RKM) algorithm to deal with multi-view data, based on the K-means algorithm, add regularization terms, which can avoid data overfitting. Through numerical analysis, the RKM significantly improves clustering performance to the comparison methods.