COMPACT: A Comparative Package for Clustering Assessment [chapter]

Roy Varshavsky, Michal Linial, David Horn
<span title="">2005</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
There exist numerous algorithms that cluster data-points from largescale 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
more &raquo; ... 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 highdimensional 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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.1007/11576259_18</a> <a target="_blank" rel="external noopener" href="">fatcat:ff6qxufxpjcxpjbcyfsinacvcq</a> </span>
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