Accurate and fast approaches for automatic ECG data classification are vital for clinical diagnosis of heart disease. To this end, we propose a novel multistage algorithm that combines various procedures for dimensionality reduction, consensus clustering of randomized samples and fast supervised classification algorithms for processing of the highly dimensional large ECG datasets. We carried ou...

Many consensus clustering methods have been applied in different areas such as pattern recognition, machine learning, information theory and bioinformatics. However, few methods have been used for chemical compounds clustering. In this paper, an information theory and voting based algorithm (Adaptive Cumulative Voting-based Aggregation Algorithm A-CVAA) was examined for combining multiple clust...

We present the first application of the quality threshold (QT) clustering algorithm to mass spectrometry (MS) data. The unique abilities of QT clustering to yield precision nodes that are commensurate with the mass measurement precision of the instrument are exploited to generate a consensus spectrum out of multiple replicate spectra. The spectral dot product and confidence intervals are used a...

The rapid advances of high-throughput sequencing technologies dramatically prompted metagenomic studies of microbial communities that exist at various environments. Fundamental questions in metagenomics include the identities, composition and dynamics of microbial populations and their functions and interactions. However, the massive quantity and the comprehensive complexity of these sequence d...

Sequential data clustering provides useful techniques for condensing and summarizing information conveyed in sequential data, which is demanded in various fields ranging from time series analysis to video clip understanding. In this chapter, we propose a novel approach to sequential data clustering by combining multiple competitive learning networks incorporated by various representations of se...

A very promising approach to reach a robust partitioning is to use ensemble-based learning. In this way, the classification/clustering task is more reliable, because the classifiers/clusterers in the ensemble cover the faults of each other. The common policy in clustering ensemble based learning is to generate a set of primary partitionings that are different from each other. These primary part...

Recent work on constrained data clustering have shown that the incorporation of pairwise constraints, such as must-link and cannot-link constraints, increases the accuracy of single run data clustering methods. It was also shown that the quality of a consensus partition, resulting from the combination of multiple data partitions, is usually superior than the quality of the partitions produced b...

Clustering ensembles have emerged as a powerful method for improving both the robustness and the stability of unsupervised classification solutions. However, finding a consensus clustering from multiple partitions is a difficult problem that can be approached from graph-based, combinatorial or statistical perspectives. We offer a probabilistic model of consensus using a finite mixture of multin...

Text clustering is a useful and inexpensive way to organize vast text repositories into meaningful topics categories. However, there is little consensus on which clustering techniques work best and in what circumstances because researchers do not use the same evaluation methodologies and document collections. Furthermore, text clustering offers a low cost alternative to supervised classificatio...