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dc.contributor.authorFahy, Conoren
dc.contributor.authorYang, Shengxiangen
dc.date.accessioned2016-09-21T09:25:11Z
dc.date.available2016-09-21T09:25:11Z
dc.date.issued2016-10-08
dc.identifier.citationFahy, C. and Yang, S. (2016) Dynamic stream clustering using ants. In: P. Angelov, A. Gegov, C. Jayne, and Q. Shen (Eds.), Advances in Computational Intelligence Systems, Volume 513 of the series Advances in Intelligent Systems and Computing, Chapter 32, pp. 495-508en
dc.identifier.urihttp://hdl.handle.net/2086/12626
dc.description.abstractData stream mining is the process of extracting knowledge from continuous sequences of data. It differs from conventional data mining in that a stream is potentially unbounded, data points arrive online and each data point can be examined only once. Furthermore, in non-stationary environments the statistical properties of the data can change over time. This paper presents a bio-inspired approach to clustering non-stationary data streams. The proposed algorithm, Ant-Colony Stream Clustering (ACSC), is based on the concept of artificial ants which identify clusters as nests of micro-clusters in dense areas of the data. Micro-clusters are N-dimensional spheres with a maximum radius ε. In ACSC the ε-neighbourhood, crucial in density clustering, is adaptive and doesn’t require expert, data-dependent tuning. The algorithm uses the sliding window model and summary statistics for each window are stored offline. Experimental results over real and synthetic datasets show that the clustering quality of ACSC is comparable or favourable to leading stream-clustering algorithms while requiring fewer parameters and considerably less computation.en
dc.language.isoenen
dc.publisherSpringeren
dc.subjectData streamen
dc.subjectClassificationen
dc.subjectAnten
dc.titleDynamic based stream clustering using antsen
dc.typeConferenceen
dc.identifier.doihttp://dx.doi.org/10.1007/978-3-319-46562-3_32
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.explorer.multimediaNoen
dc.funderEPSRC (Engineering and Physical Sciences Research Council)en
dc.projectidEP/K001310/1en
dc.cclicenceCC-BY-NCen
dc.date.acceptance2016-07-13en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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