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 …

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 …

Since the inception of the concept of collaborative fuzzy clustering (CFC), many related
ideas and algorithms have been proposed. In this study, we offer a synthetic view of this …

Among the multi-source data clustering tasks, there is a kind of frequently encountered tasks
where only one of the multi-source datasets is available for sake of privacy and other …

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

In this paper, we focus on collaborative clustering methods based on topological
approaches, such as self-organizing maps (SOM) and self-organizing maps based on a …

The aim of collaborative clustering is to reveal the common underlying structure of data
spread across multiple sites by applying different clustering algorithms and therefore …

Being one of the most important models for multisource data clustering, Horizontal
Collaborative Fuzzy Clustering (HCFC) implements the clustering by fusing the knowledge …

Graph clustering techniques are very useful for detecting densely connected groups in large
graphs. Many existing graph clustering methods mainly focus on the topological structure …

This study presents a new knowledge-based fuzzy clustering called semi-supervised
collaborative fuzzy clustering (S^ 2 2 CFC), which emphasizes aggregating diverse …