Ensemble learning combines several individual models to obtain better generalization
performance. Currently, deep learning architectures are showing better performance …

In expert systems, data mining methods are algorithms that simulate humans' problem-
solving capabilities. Clustering methods as unsupervised machine learning methods are …

Cluster ensemble is one of the main branches in the ensemble learning area which is an
important research focus in recent years. The objective of cluster ensemble is to combine …

Numerous works implemented multi-view clustering algorithms in document clustering. A
challenging problem in document clustering is the similarity metric. Existing multi-view …

Ensemble clustering has emerged as a powerful framework for analyzing heterogeneous
and complex data. Despite the abundance of existing schemes, co-association matrix-based …

Semi-supervised clustering (ie, clustering with knowledge-based constraints) has emerged
as an important variant of the traditional clustering paradigms. However, most existing semi …

Network Anomaly Detection plays an important part in network security. Among the state-of-
the-art approaches, unsupervised anomaly detection is effective when dealing with …

We present a framework for an explainable and statistically validated ensemble clustering
model applied to Traumatic Brain Injury (TBI). The objective of our analysis is to identify …

Multi-view clustering has become an important extension of ensemble clustering. In multi-
view clustering, we apply clustering algorithms on different views of the data to obtain …

Recommending online news articles has become a promising research direction as the
Internet provides fast access to real-time information from multiple sources around the world …