Tight clustering for large datasets with an application to gene expression data View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Bikram Karmakar, Sarmistha Das, Sohom Bhattacharya, Rohan Sarkar, Indranil Mukhopadhyay

ABSTRACT

This article proposes a practical and scalable version of the tight clustering algorithm. The tight clustering algorithm provides tight and stable relevant clusters as output while leaving a set of points as noise or scattered points, that would not go into any cluster. However, the computational limitation to achieve this precise target of tight clusters prohibits it from being used for large microarray gene expression data or any other large data set, which are common nowadays. We propose a pragmatic and scalable version of the tight clustering method that is applicable to data sets of very large size and deduce the properties of the proposed algorithm. We validate our algorithm with extensive simulation study and multiple real data analyses including analysis of real data on gene expression. More... »

PAGES

3053

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-019-39459-w

DOI

http://dx.doi.org/10.1038/s41598-019-39459-w

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112440423

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30816195


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