Subspace clustering refers to the task of identifying clusters of similar objects or data records
(vectors) where the similarity is defined with respect to a subset of the attributes (ie, a …

Clustering has been widely used to identify possible structures in data and help users to
understand data in an unsupervised manner. Traditional clustering methods often provide a …

Clustering has been a major research topic in the field of machine learning, one to which
Deep Learning has recently been applied with significant success. However, an aspect of …

Many clustering algorithms only find one clustering solution. However, data can often be
grouped and interpreted in many different ways. This is particularly true in the high …

Although clustering has been studied for several decades, the fundamental problem of a
valid evaluation has not yet been solved. The sound evaluation of clustering results in …

Multi-view clustering aims at integrating complementary information from multiple
heterogeneous views to improve clustering results. Existing multi-view clustering solutions …

A clustering may be considered as fair on pre-specified sensitive attributes if the proportions
of sensitive attribute groups in each cluster reflect that in the dataset. In this paper, we …

Existing models for cluster analysis typically consist of a number of attributes that describe
the objects to be partitioned and one single latent variable that represents the clusters to be …

Multiple clustering aims at exploring alternative clusterings to organize the data into
meaningful groups from different perspectives. Existing multiple clustering algorithms are …

The aim of data mining is to find novel and actionable insights in data. However, most
algorithms typically just find a single (possibly non-novel/actionable) interpretation of the …