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 …

In spite of some attempts at improving the quality of the clustering ensemble methods, it
seems that little research has been devoted to the selection procedure within the fuzzy …

Many stability measures, such as Normalized Mutual Information (NMI), have been
proposed to validate a set of partitionings. It is highly possible that a set of partitionings may …

Each clustering algorithm usually optimizes a qualification metric during its learning process.
The qualification metric in traditional clustering algorithms considers all the features of under …

Conventional clustering ensemble algorithms employ a set of primary results; each result
includes a set of clusters which are emerged from data. Given a large number of available …

The wisdom of crowds (WOCs), as a theory in the social science, gets a new paradigm in
computer science. The WOC theory explains that the aggregate decision made by a group is …

Abstract The Wisdom of Crowds is a phenomenon described in social science that suggests
four criteria applicable to groups of people. It is claimed that, if these criteria are satisfied …

This research introduces a new strategy in cluster ensemble selection by using
Independency and Diversity metrics. In recent years, Diversity and Quality, which are two …

Term clustering tries to group words based on the similarity criterion between words, so that
the groups can be used as the dimensions of the vector space in the text categorization. We …

Deriving classification information from large databases presents several challenges. The
current methods used to classify a large dataset have the disadvantage of requiring long …