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 …

Multi-view clustering can explore consistent information from different views to guide
clustering. Most existing works focus on pursuing shallow consistency in the feature space …

Studies the problem of maintaining materialized views of graph structured data. The base
data consists of records containing identifiers of other records. The data could represent …

Unsupervised machine learning approaches involving several clustering algorithms working
together to tackle difficult data sets are a recent area of research with a large number of …

Different data types are frequently included in clinical data. Applying machine learning
algorithms to mixed data can be difficult and impact the output accuracy and quality. This …

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 structure of data distributed on
different sites. In this paper, we present a formalism of topological collaborative clustering …

The ability to build more robust clustering from many clustering models with different
solutions is relevant in scenarios with privacy-preserving constraints, where data features …

Clustering is a machine learning technique that seeks to uncover the intrinsic patterns from a
dataset by grouping related objects together. While clustering has gained significant …

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 …