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Please use this identifier to cite or link to this item: http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/8148
Title: Anytime clustering of data streams while handling noise and concept drift
Authors: Goyal, Poonam
Goyal, Navneet
Challa, Jagat Sesh
Keywords: Stream data mining
Computer Science
Anytime Mining
Multiport streams
Clustering streaming data
Issue Date: Mar-2021
Publisher: Taylor & Francis
Abstract: Clustering of data streams has become very popular in recent times, owing to rapid rise of real-time streaming utilities that produce large amounts of data at varying inter-arrival rates. We propose AnyClus, a framework for anytime clustering of data streams. AnyClus uses a proposed variant of R-tree, AnyRTree, to capture the incoming stream objects arriving at variable rate, and to index them in the form of micro-clusters of hierarchical fashion. The leaf-level micro-clusters produced are aggregated and stored in a logarithmic tilted-time window framework (TTWF). Our extensive experimental analysis shows (i) the capability of AnyClus in handling variable stream speeds (upto 250k objects/second); (ii) its ability to produce micro-clusters of high purity (≈1) and compactness; (iii) effectiveness of AnyRTree in handling noise, capturing concept drift and preservation of spatial locality in the indexing of micro-clusters, when compared to the existing methods. We also propose a parallel framework, Any-MP-Clus, for anytime clustering of multiport data streams over commodity clusters. Any-MP-Clus uses AnyRTree at each computing node of the cluster (for each stream-port) and maintains the aggregated micro-clusters in TTWF. The experimental results on datasets of billions scale show that Any-MP-Clus is scalable, efficient and produces clustering of higher quality.
URI: https://www.tandfonline.com/doi/full/10.1080/0952813X.2021.1882001
http://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/8148
Appears in Collections:Department of Computer Science and Information Systems

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