MicroGRID: An Accurate and Efficient Real-Time Stream Data Clustering with Noise

Tari, Z, Thompson, A, Almusallam, N, Bertok, P and Mahmood, A 2018, 'MicroGRID: An Accurate and Efficient Real-Time Stream Data Clustering with Noise', in Dinh Phung, Vincent S. Tseng, Geoffrey I. Webb, Bao Ho, Mohadeseh Ganji, Lida Rashidi (ed.) Proceeding of the 22nd Pacific-Asia Conference Advances of Knowledge Discovery and Data Mining (PAKDD 2018) Part II, Melbourne, Australia, 3-6 June 2018, pp. 483-494.


Document type: Conference Paper
Collection: Conference Papers

Title MicroGRID: An Accurate and Efficient Real-Time Stream Data Clustering with Noise
Author(s) Tari, Z
Thompson, A
Almusallam, N
Bertok, P
Mahmood, A
Year 2018
Conference name PAKDD 2018: Volume: 10938
Conference location Melbourne, Australia
Conference dates 3-6 June 2018
Proceedings title Proceeding of the 22nd Pacific-Asia Conference Advances of Knowledge Discovery and Data Mining (PAKDD 2018) Part II
Editor(s) Dinh Phung, Vincent S. Tseng, Geoffrey I. Webb, Bao Ho, Mohadeseh Ganji, Lida Rashidi
Publisher Springer
Place of publication Cham, Switzerland
Start page 483
End page 494
Total pages 12
Abstract Data stream clustering aims to produce clusters from a data-stream in a real-time. Many of existing algorithms focus however on solving a single problem, leaving anomalous noise in data streams at the wayside. This paper describes the MicroGRID approach to cluster data from single data-streams to handle noisy data streams, accurately identifying and separating noise-affected data points from outlier points. In particular, MicroGRID utilises a combination of micro-cluster and grid-based prospectives, an approach that has not been attempted when clustering data-streams. The experimental results clearly show that MicroGRID significantly outperforms the baseline methods: MicroGRID is up 87% faster and up to 80% more accurate clustering outputs.
Subjects Distributed and Grid Systems
Keyword(s) Micro-clustering
Noise
DOI - identifier 10.1007/978-3-319-93037-4_38
Copyright notice © Springer International Publishing AG, part of Springer Nature 2018
ISBN 9783319930367
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