This work is motivated by the need for consensus clustering methods using multiple datasets, applicable to microarray data. It introduces a new method for clustering samples with similar genetic profiles, in an unsupervised fashion, using information from two or more datasets. The method was tested using two breast cancer gene expression microarray datasets, with 295 and 249 samples; and 12,325...

To be successful, cybercriminals must figure out how to scale their scams. They duplicate content on new websites, often staying one step ahead of defenders that shut down past schemes. For some scams, such as phishing and counterfeitgoods shops, the duplicated content remains nearly identical. In others, such as advanced-fee fraud and online Ponzi schemes, the criminal must alter content so th...

Clustering in graphs aims to group vertices with similar patterns of connections. Applications include discovering communities and latent structures in graphs. Many algorithms have been proposed to find graph clusterings, but an open problem is the need for suitable comparison measures to quantitatively validate these algorithms, performing consensus clustering and to track evolving (graph) clu...

Modern cognitive experiments in functional Magnetic Resonance Imaging (fMRI) often aim at understanding the temporal dynamics of the brain response in regions activated by a given stimulus. The study of the variability of the hemodynamic response function (HRF) and its characteristics can provide some answers. In this context, we aim at improving the accuracy of the HRF estimation. To do so, we...

In this paper, we investigate the opinion dynamics model of social impact theory on triad scale free network with power law degree distribution and tunable clustering coefficient. Based on this opinion dynamic model, we try to observe the clustering coefficient influence on opinion formation by adjusting the triad formation parameter. Simulation result shows that by adjusting triad scale free n...

Learning Object Repositories are increasingly being used in learning systems to provide high-quality, reusable educational materials. A relevant data mining problem associated with the automatic categorization of learning objects is the discovery of intrinsic classes based on the textual contents of the meta-data records. In this paper, we present a cluster ensemble method, that is applicable f...

Several real-world networks of interest, such as social and biological networks, are modular in nature. Most of these networks also possess the scale-free property, which makes the task of detecting and isolating communities from these networks difficult. The application of traditional clustering algorithms on these networks has not yielded a great deal of success. In this paper, we apply an en...

To devise efficient solutions for approximating a mean partition in consensus clustering, Dimitriadou et al. [3] presented a necessary condition of optimality for a consensus function based on least square distances. We show that their result is pivotal for deriving interesting properties of consensus clustering beyond optimization. For this, we present the necessary condition of optimality in ...

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 generate more robust and stable clustering results compared to a single clustering approach, perform distributed computing under privacy or sharing constraints, or reuse existing knowledge. This paper describes...