rEMM: Extensible Markov Model for Data Stream Clustering in R
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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Foundation for Open Access Statistics
2010-10-01
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| Series: | Journal of Statistical Software |
| Subjects: | |
| Online Access: | http://www.jstatsoft.org/v35/i05/paper |
| Summary: | 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 <b>R</b> extension package <b>rEMM</b> which implements EMM and we discuss some examples and applications. |
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| ISSN: | 1548-7660 |