Finding and Tracking Multi-Density Clusters in Online Dynamic Data Streams

Change is one of the biggest challenges in dynamic stream mining. From a data-mining perspective, adapting and tracking change is desirable in order to understand how and why change has occurred. Clustering, a form of unsupervised learning, can be used to identify the underlying patterns in a stream...

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Tác giả chính:	Fahy, Conor
Đồng tác giả:	Yang, Shengxiang
Định dạng:	BB
Ngôn ngữ:	English
Thông tin xuất bản:	IEEE Xplore 2020
Chủ đề:	Data stream clustering multi-density clustering concept drift concept evolution swarm intelligence change detection
Truy cập trực tuyến:	http://tailieuso.tlu.edu.vn/handle/DHTL/9783
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spelling	oai:localhost:DHTL-97832020-11-25T08:18:08Z Finding and Tracking Multi-Density Clusters in Online Dynamic Data Streams Fahy, Conor Yang, Shengxiang Data stream clustering multi-density clustering concept drift concept evolution swarm intelligence change detection Change is one of the biggest challenges in dynamic stream mining. From a data-mining perspective, adapting and tracking change is desirable in order to understand how and why change has occurred. Clustering, a form of unsupervised learning, can be used to identify the underlying patterns in a stream. Density-based clustering identifies clusters as areas of high density separated by areas of low density. This paper proposes a Multi-Density Stream Clustering (MDSC) algorithm to address these two problems; the multi-density problem and the problem of discovering and tracking changes in a dynamic stream. MDSC consists of two on-line components; discovered, labelled clusters and an outlier buffer. Incoming points are assigned to a live cluster or passed to the outlier buffer. New clusters are discovered in the buffer using an ant-inspired swarm intelligence approach. The newly discovered cluster is uniquely labelled and added to the set of live clusters. Processed data is subject to an ageing function and will disappear when it is no longer relevant. MDSC is shown to perform favourably to state-of-the-art peer stream-clustering algorithms on a range of real and synthetic data-streams. Experimental results suggest that MDSC can discover qualitatively useful patterns while being scalable and robust to noise. https://doi.org/10.1109/TBDATA.2019.2922969 2020-11-25T08:17:20Z 2020-11-25T08:17:20Z 2019 BB http://tailieuso.tlu.edu.vn/handle/DHTL/9783 en IEEE Transactions on Big Data, (2019), pp 15, issue 99 application/pdf IEEE Xplore
institution	Trường Đại học Thủy Lợi
collection	DSpace
language	English
topic	Data stream clustering multi-density clustering concept drift concept evolution swarm intelligence change detection
spellingShingle	Data stream clustering multi-density clustering concept drift concept evolution swarm intelligence change detection Fahy, Conor Finding and Tracking Multi-Density Clusters in Online Dynamic Data Streams
description	Change is one of the biggest challenges in dynamic stream mining. From a data-mining perspective, adapting and tracking change is desirable in order to understand how and why change has occurred. Clustering, a form of unsupervised learning, can be used to identify the underlying patterns in a stream. Density-based clustering identifies clusters as areas of high density separated by areas of low density. This paper proposes a Multi-Density Stream Clustering (MDSC) algorithm to address these two problems; the multi-density problem and the problem of discovering and tracking changes in a dynamic stream. MDSC consists of two on-line components; discovered, labelled clusters and an outlier buffer. Incoming points are assigned to a live cluster or passed to the outlier buffer. New clusters are discovered in the buffer using an ant-inspired swarm intelligence approach. The newly discovered cluster is uniquely labelled and added to the set of live clusters. Processed data is subject to an ageing function and will disappear when it is no longer relevant. MDSC is shown to perform favourably to state-of-the-art peer stream-clustering algorithms on a range of real and synthetic data-streams. Experimental results suggest that MDSC can discover qualitatively useful patterns while being scalable and robust to noise.
author2	Yang, Shengxiang
author_facet	Yang, Shengxiang Fahy, Conor
format	BB
author	Fahy, Conor
author_sort	Fahy, Conor
title	Finding and Tracking Multi-Density Clusters in Online Dynamic Data Streams
title_short	Finding and Tracking Multi-Density Clusters in Online Dynamic Data Streams
title_full	Finding and Tracking Multi-Density Clusters in Online Dynamic Data Streams
title_fullStr	Finding and Tracking Multi-Density Clusters in Online Dynamic Data Streams
title_full_unstemmed	Finding and Tracking Multi-Density Clusters in Online Dynamic Data Streams
title_sort	finding and tracking multi-density clusters in online dynamic data streams
publisher	IEEE Xplore
publishDate	2020
url	http://tailieuso.tlu.edu.vn/handle/DHTL/9783
work_keys_str_mv	AT fahyconor findingandtrackingmultidensityclustersinonlinedynamicdatastreams
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Finding and Tracking Multi-Density Clusters in Online Dynamic Data Streams

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