As a promising way for heterogeneous data analytics, consensus clustering has attracted
increasing attention in recent decades. Among various excellent solutions, the co …

Ensemble clustering, also known as consensus clustering, is emerging as a promising
solution for multi-source and/or heterogeneous data clustering. The co-association matrix …

Ensemble Clustering (EC) aims to integrate multiple Basic Partitions (BPs) of the same
dataset into a consensus one. It could be transformed as a graph partition problem on the co …

Ensemble Clustering (EC) is an important topic for data cluster analysis. It targets to
integrate multiple Basic Partitions (BPs) of a particular dataset into a consensus partition …

Multi-view learning has attracted increasing attention in recent years, and the existing multi-
view learning methods learn a consensus result by collecting all views. These methods have …

In this paper, we propose a new spectral clustering method, referred to as Spectral
Embedded Clustering (SEC), to minimize the normalized cut criterion in spectral clustering …

This paper focuses on scalability and robustness of spectral clustering for extremely large-
scale datasets with limited resources. Two novel algorithms are proposed, namely, ultra …

Spectral clustering methods perform well in cases where classical methods (K-means,
single linkage, etc.) fail. However, for very non-compact clusters, they also tend to have …

During the past decades, many spectral clustering algorithms have been proposed.
However, their high computational complexities hinder their applications on large-scale …

In many clustering problems, we have access to multiple views of the data each of which
could be individually used for clustering. Exploiting information from multiple views, one can …