Algorithms for clustering data
Algorithms for clustering data
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Clustering algorithms optimizer: a framework for large datasets
ISBRA'07 Proceedings of the 3rd international conference on Bioinformatics research and applications
Distilling relevant documents by means of dynamic quantum clustering
ICTIR'11 Proceedings of the Third international conference on Advances in information retrieval theory
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There exist numerous algorithms that cluster data-points from large-scale genomic experiments such as sequencing, gene-expression and proteomics. Such algorithms may employ distinct principles, and lead to different performance and results. The appropriate choice of a clustering method is a significant and often overlooked aspect in extracting information from large-scale datasets. Evidently, such choice may significantly influence the biological interpretation of the data. We present an easy-to-use and intuitive tool that compares some clustering methods within the same framework. The interface is named COMPACT for Comparative-Package-for-Clustering-Assessment. COMPACT first reduces the dataset’s dimensionality using the Singular Value Decomposition (SVD) method, and only then employs various clustering techniques. Besides its simplicity, and its ability to perform well on high-dimensional data, it provides visualization tools for evaluating the results. COMPACT was tested on a variety of datasets, from classical benchmarks to large-scale gene-expression experiments. COMPACT is configurable and expendable to newly added algorithms.