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Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
rEMM: Extensible Markov Model for Data Stream Clustering in R | Hahsler | Journal of Statistical Software
Authors: Michael Hahsler, Margaret H. Dunham
Title: rEMM: Extensible Markov Model for Data Stream Clustering in R
Abstract: Clustering streams of continuously arriving data has become an important application of data mining in recent years and efficient algorithms have been proposed by several researchers. However, clustering alone neglects the fact that data in a data stream is not only characterized by the proximity of data points which is used by clustering, but also by a temporal component. The extensible Markov model (EMM) adds the temporal component to data stream clustering by superimposing a dynamically adapting Markov chain. In this paper we introduce the implementation of the R extension package rEMM which implements EMM and we discuss some examples and applications.

Page views:: 4936. Submitted: 2009-05-26. Published: 2010-07-16.
Paper: rEMM: Extensible Markov Model for Data Stream Clustering in R     Download PDF (Downloads: 5183)
Supplements:
rEMM_1.0-0.tar.gz: R source package Download (Downloads: 740; 642KB)
v35i05.R: R example code from the paper Download (Downloads: 763; 3KB)

DOI: 10.18637/jss.v035.i05

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Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.