Clustering hashtags based on their semantics is an important problem with many applications. The uncontrolled usage of hashtags in social media, however, makes the quality of semantics and the frequency of usage vary a lot, and this poses a challenge to the current approaches which capitalize on either the lexical semantics of a hashtag (by using metadata) or the contextual semantics of a hasht...

The uncontrolled usage of hashtags in social media makes them vary a lot in the quality of semantics and the frequency of usage. Such variations pose a challenge to the current approaches which capitalize on either the lexical semantics of a hashtag by using metadata or the contextual semantics of a hashtag by using the texts associated with a hashtag. This thesis presents a hybrid approach to ...

Ensemble clustering is a novel research field that extends to unsupervised learning the approach originally developed for classification and supervised learning problems. In particular ensemble clustering methods have been developed to improve the robustness and accuracy of clustering algorithms, as well as the ability to capture the structure of complex data. In many clustering applications an...

Discovery of communities in complex networks is a fundamental data analysis task in various domains. Generative models are a promising class of techniques for identifying modular properties from networks, which has been actively discussed recently. However, most of them cannot preserve the degree sequence of networks, which will distort the community detection results. Rather than using a block...

UNLABELLED Standard and Consensus Clustering Analysis Tool for Microarray Data (SC²ATmd) is a MATLAB-implemented application specifically designed for the exploration of microarray gene expression data via clustering. Implementation of two versions of the clustering validation method figure of merit allows for performance comparisons between different clustering algorithms, and tailors the clus...

Background: In recent years unsupervised ensemble clustering methods have been successfully applied to DNA microarray data analysis to improve the accuracy and the reliability of clustering results. Nevertheless, a major problem is represented by the fact that classes of functionally correlated examples (e.g. subclasses of diseases characterized at bio-molecular level) are not in general clearl...

A novel approach rooted on the notion of consensus clustering, a strategy developed for community detection in complex networks, is proposed to cope with the heterogeneity that characterizes connectivity matrices in health and disease. The method can be summarized as follows: (a) define, for each node, a distance matrix for the set of subjects by comparing the connectivity pattern of that node ...

MOTIVATION Consensus clustering, also known as cluster ensemble, is one of the important techniques for microarray data analysis, and is particularly useful for class discovery from microarray data. Compared with traditional clustering algorithms, consensus clustering approaches have the ability to integrate multiple partitions from different cluster solutions to improve the robustness, stabili...

MOTIVATION Reliable identification of protein families is key to phylogenetic analysis, functional annotation and the exploration of protein function diversity in a given phylogenetic branch. As more and more complete genomes are sequenced, there is a need for powerful and reliable algorithms facilitating protein families construction. RESULTS We have formulated the problem of protein familie...

Clustering ensemble, also referred to as consensus clustering, has emerged a method of combining an ensemble different clusterings derive final clustering that is better quality and robust than any single in the ensemble. Normally algorithms literature combine all without learning But by one can define merit or even cluster it, forming consensus. In this work, we propose cluster-level surprisal...