Clustering is one type of unsupervised learning where the goal is to partition the set of
objects into groups called clusters. Faced to the difficulty to design a general purpose …

The purpose of this article is to introduce a new collaborative multi-view clustering approach
based on a probabilistic model. The aim of collaborative clustering is to reveal the common …

The aim of collaborative clustering is to reveal the common underlying structure of data
spread across multiple data sites by applying clustering techniques. The idea of …

Collaborative clustering is a recent field of Machine Learning that shows similarities with
both transfer learning and ensemble learning. It uses two-step approaches where different …

The aim of collaborative clustering is to enhance the performances of clustering algorithms
by enabling them to work together and exchange their information to tackle difficult data sets …

Non-negative matrix factorization (NMF) is an unsupervised algorithm for clustering where a
non-negative data matrix is factorized into (usually) two matrices with the property that all the …

Collaborative clustering aims to find a common data structure between several distributed
data sets governed by different privacy constraints and technical limitations that prohibit a …

In this paper, we propose a collaborative framework that is able to solve multi-view and
alternative clustering problems using some clustering ensemble and semi-supervised …

In the collaborative clustering framework, the hope is that by combining several clustering
solutions, each one with its own bias and imperfections, one will get a better overall solution …

Different companies may not be allowed to treat data together given restrictions of security,
privacy or other technical reasons. In order to make better use of information from different …