Ensemble methods are considered the state‐of‐the art solution for many machine learning
challenges. Such methods improve the predictive performance of a single model by training …

Despite significant successes achieved in knowledge discovery, traditional machine
learning methods may fail to obtain satisfactory performances when dealing with complex …

Cluster ensemble has proved to be a good alternative when facing cluster analysis
problems. It consists of generating a set of clusterings from the same dataset and combining …

Class imbalance is an important problem, encountered in machine learning applications,
where one class (named as, the minority class) has extremely small number of instances …

Ensembles for unsupervised outlier detection is an emerging topic that has been neglected
for a surprisingly long time (although there are reasons why this is more difficult than …

Cluster ensembles have been shown to be better than any standard clustering algorithm at
improving accuracy and robustness across different data collections. This meta-learning …

Cluster analysis deals with the automatic discovery of the grou** of a set of patterns.
Despite more than 40 years of research, there are still many challenges in data clustering …

In recent years, internet technologies and its rapid growth have created a paradigm of digital
services. In this new digital world, users suffer due to the information overload problem and …

Traditional cluster ensemble approaches have three limitations:() They do not make use of
prior knowledge of the datasets given by experts.() Most of the conventional cluster …

Cluster ensembles combine multiple clusterings of a set of objects into a single consolidated
clustering, often referred to as the consensus solution. Consensus clustering can be used to …