Data Stream Clustering With Affinity Propagation
Data stream clustering provides insights into the underlying patterns of data flows. This paper focuses on selecting the best representatives from clusters of streaming data. There are two main challenges: how to cluster with the best representatives and how to handle the evolving patterns that are...
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| Veröffentlicht in: | IEEE transactions on knowledge and data engineering 2014-07, Vol.26 (7), p.1644-1656 |
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| container_title | IEEE transactions on knowledge and data engineering |
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| creator | Xiangliang Zhang Furtlehner, Cyril Germain-Renaud, Cecile Sebag, Michele |
| description | Data stream clustering provides insights into the underlying patterns of data flows. This paper focuses on selecting the best representatives from clusters of streaming data. There are two main challenges: how to cluster with the best representatives and how to handle the evolving patterns that are important characteristics of streaming data with dynamic distributions. We employ the Affinity Propagation (AP) algorithm presented in 2007 by Frey and Dueck for the first challenge, as it offers good guarantees of clustering optimality for selecting exemplars. The second challenging problem is solved by change detection. The presented StrAP algorithm combines AP with a statistical change point detection test; the clustering model is rebuilt whenever the test detects a change in the underlying data distribution. Besides the validation on two benchmark data sets, the presented algorithm is validated on a real-world application, monitoring the data flow of jobs submitted to the EGEE grid. |
| doi_str_mv | 10.1109/TKDE.2013.146 |
| format | Article |
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This paper focuses on selecting the best representatives from clusters of streaming data. There are two main challenges: how to cluster with the best representatives and how to handle the evolving patterns that are important characteristics of streaming data with dynamic distributions. We employ the Affinity Propagation (AP) algorithm presented in 2007 by Frey and Dueck for the first challenge, as it offers good guarantees of clustering optimality for selecting exemplars. The second challenging problem is solved by change detection. The presented StrAP algorithm combines AP with a statistical change point detection test; the clustering model is rebuilt whenever the test detects a change in the underlying data distribution. 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(IEEE) Jul 2014</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c358t-6f233230a3d9405a6c81b8b1885c1ec9c020e6605e1fe96ffbde487accbebbe33</citedby><cites>FETCH-LOGICAL-c358t-6f233230a3d9405a6c81b8b1885c1ec9c020e6605e1fe96ffbde487accbebbe33</cites><orcidid>0000-0002-3574-5665</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6585253$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>230,314,780,784,796,885,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6585253$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://inria.hal.science/hal-00862941$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Xiangliang Zhang</creatorcontrib><creatorcontrib>Furtlehner, Cyril</creatorcontrib><creatorcontrib>Germain-Renaud, Cecile</creatorcontrib><creatorcontrib>Sebag, Michele</creatorcontrib><title>Data Stream Clustering With Affinity Propagation</title><title>IEEE transactions on knowledge and data engineering</title><addtitle>TKDE</addtitle><description>Data stream clustering provides insights into the underlying patterns of data flows. This paper focuses on selecting the best representatives from clusters of streaming data. There are two main challenges: how to cluster with the best representatives and how to handle the evolving patterns that are important characteristics of streaming data with dynamic distributions. We employ the Affinity Propagation (AP) algorithm presented in 2007 by Frey and Dueck for the first challenge, as it offers good guarantees of clustering optimality for selecting exemplars. The second challenging problem is solved by change detection. The presented StrAP algorithm combines AP with a statistical change point detection test; the clustering model is rebuilt whenever the test detects a change in the underlying data distribution. Besides the validation on two benchmark data sets, the presented algorithm is validated on a real-world application, monitoring the data flow of jobs submitted to the EGEE grid.</description><subject>Affinity Propagation</subject><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Change detection algorithms</subject><subject>Clustering algorithms</subject><subject>Computational modeling</subject><subject>Computer Science</subject><subject>Data models</subject><subject>Data Stream Clustering</subject><subject>Machine Learning</subject><subject>Monitoring</subject><subject>Optimization</subject><subject>Reservoirs</subject><issn>1041-4347</issn><issn>1558-2191</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kE1LAzEQhoMoWKtHT14WPHnYmtl8bHIsbbViQcGKx5BNkzal3a1JKvTfu8uKp3kZHl5mHoRuAY8AsHxcvk5nowIDGQHlZ2gAjIm8AAnnbcYUckpoeYmuYtxijEUpYIDwVCedfaRg9T6b7I4x2eDrdfbl0yYbO-drn07Ze2gOeq2Tb-prdOH0LtqbvzlEn0-z5WSeL96eXybjRW4IEynnriCkIFiTlaSYaW4EVKICIZgBa6TBBbacY2bBWcmdq1aWilIbU9mqsoQM0UPfu9E7dQh-r8NJNdqr-Xihul37AS8khR9o2fuePYTm-2hjUtvmGOr2PAWMSkahlLil8p4yoYkxWPdfC1h1AlUnUHUCVSuw5e963ltr_1nOBCsYIb8Dv2oC</recordid><startdate>20140701</startdate><enddate>20140701</enddate><creator>Xiangliang Zhang</creator><creator>Furtlehner, Cyril</creator><creator>Germain-Renaud, Cecile</creator><creator>Sebag, Michele</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| subjects | Affinity Propagation Algorithms Artificial Intelligence Change detection algorithms Clustering algorithms Computational modeling Computer Science Data models Data Stream Clustering Machine Learning Monitoring Optimization Reservoirs |
| title | Data Stream Clustering With Affinity Propagation |
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