PeerJ Computer ScienceBioinformaticsPeer-reviewed journal article
ClusterEnG: An interactive educational web resource for clustering big data
Mohith ManjunathiD, Yi ZhangiD, Yeonsung Kim, Steve H. Yeo, Omar T. Sobh, Nathan Russell, Christian Followell, Colleen BushelliD, Umberto RavaioliiD, Jun S. SongiD
Abstract
SummaryClustering is one of the most common techniques used in data analysis to discover hidden structures by grouping together data points that are similar in some measure into clusters. Although there are many programs available for performing clustering, a single web resource that provides both state-of-the-art clustering methods and interactive visualizations is lacking. ClusterEnG (acronym for Clustering Engine for Genomics) provides an interface for clustering big data and interactive visualizations including 3D views, cluster selection and zoom features. ClusterEnG also aims at educating the user about the similarities and differences between various clustering algorithms and provides clustering tutorials that demonstrate potential pitfalls of each algorithm. The web resource will be particularly useful to scientists who are not conversant with computing but want to understand the structure of their data in an intuitive manner.\n\nAvailabilityClusterEnG is part of a bigger project called KnowEnG (Knowledge Engine for Genomics) and is available at http://education.knoweng.org/clustereng.\n\nContactsongi@illinois.edu
- research focus: software / tool
- research focus: benchmarking
- research focus: database / resource
- approach: experimental assay
- Citations
- 10
- FWCI
- 0.63
- Field percentile
- top 30%
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