A Feature-Reduction Multi-View k-Means Clustering Algorithm

by   Kristina P Sinaga, et al.

The k-means clustering algorithm is the oldest and most known method in cluster analysis. It has been widely studied with various extensions and applied in a variety of substantive areas. Since internet, social network, and big data grow rapidly, multi-view data become more important. For analyzing multi-view data, various multi-view k-means clustering algorithms have been studied. However, most of multi-view k-means clustering algorithms in the literature cannot give feature reduction during clustering procedures. In general, there often exist irrelevant feature components in multi-view data sets that may cause bad performance for these clustering algorithms. There also exists high feature dimension in multi-view data sets so it is necessary to consider reducing its dimension for clustering algorithms. In this paper, a learning mechanism for the multi-view k-means algorithm to automatically compute individual feature weight is constructed. It can reduce these irrelevant feature components in each view. A new multi-view k-means objective function is firstly proposed for constructing the learning mechanism for feature weights in multi-view clustering. A schema for eliminating irrelevant feature(s) with small weight(s) is then considered for feature reduction. Therefore, a new type of multi-view k-means, called a feature-reduction multi-view k-means (FRMVK), is proposed. The computational complexity of FRMVK is also analyzed. Numerical and real data sets are used to compare FRMVK with other feature-weighted multi-view k-means algorithms. Experimental results and comparisons actually demonstrate the effectiveness and usefulness of the proposed FRMVK clustering algorithm.


page 2

page 3

page 4

page 6

page 8

page 9

page 13

page 14


K-means clustering for efficient and robust registration of multi-view point sets

Efficiency and robustness are the important performance for the registra...

Multi-view Data Visualisation via Manifold Learning

Non-linear dimensionality reduction can be performed by manifold learnin...

Entropy K-Means Clustering With Feature Reduction Under Unknown Number of Clusters

The k-means algorithm with its extensions is the most used clustering me...

Fairness-aware Multi-view Clustering

In the era of big data, we are often facing the challenge of data hetero...

Intrinsic Weight Learning Approach for Multi-view Clustering

Exploiting different representations, or views, of the same object for b...

Sketch and Validate for Big Data Clustering

In response to the need for learning tools tuned to big data analytics, ...

Agglomerative Neural Networks for Multi-view Clustering

Conventional multi-view clustering methods seek for a view consensus thr...

Please sign up or login with your details

Forgot password? Click here to reset