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 …

Today's data mostly does not include the knowledge of cluster number. Therefore, it is not
possible to use conventional clustering approaches to partition today's data, ie, it is …

The issue of determining “the right number of clusters” in K-Means has attracted
considerable interest, especially in the recent years. Cluster intermix appears to be a factor …

Cluster ensembles have recently emerged as a powerful alternative to standard cluster
analysis, aggregating several input data clusterings to generate a single output clustering …

Cluster ensembles offer a solution to challenges inherent to clustering arising from its ill-
posed nature. Cluster ensembles can provide robust and stable solutions by leveraging the …

Although attempts have been made to solve the problem of clustering categorical data via
cluster ensembles, with the results being competitive to conventional algorithms, it is …

Abstract Random Vector Functional Link (RVFL) Networks have received a lot of attention
due to the fast training speed as the non-iterative solution characteristic. Currently, the main …

The issue of determining 'the right number of clusters' is attracting ever growing interest. The
paper reviews published work on the issue with respect to mixture of distributions, partition …

Clustering ensembles combine multiple partitions of data into a single clustering solution of
better quality. Inspired by the success of supervised bagging and boosting algorithms, we …

High dimensional data with thousands of features present a big challenge to current
clustering algorithms. Sparsity, noise and correlation of features are common characteristics …