Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: François Role, Stanislas Morbieu, Mohamed Nadif
Title: CoClust: A Python Package for Co-Clustering
Abstract: Co-clustering (also known as biclustering), is an important extension of cluster analysis since it allows to simultaneously group objects and features in a matrix, resulting in row and column clusters that are both more accurate and easier to interpret. This paper presents the theory underlying several effective diagonal and non-diagonal co-clustering algorithms, and describes CoClust, a package which provides implementations for these algorithms. The quality of the results produced by the implemented algorithms is demonstrated through extensive tests performed on datasets of various size and balance. CoClust has been designed to complete and easily interface with popular Python machine learning libraries such as scikit-learn.

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Paper: CoClust: A Python Package for Co-Clustering     Download PDF (Downloads: 2698)
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DOI: 10.18637/jss.v088.i07

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.