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Authors: Zaher Al Aghbari 1 ; 2 ; Ayoub Al Hamadi 2 and Thar Baker 1

Affiliations: 1 University of Sharjah, United Arab Emirates ; 2 IIKT, Otto-von-Guericke-University Magdeburg, Germany

Keyword(s): Clustering subsequences, Data streams, Incremental clustering, Big Data

Abstract: Clustering subsequences of continuous data streams have a wide range of applications, such as stock market data, social data, and wireless sensor data. Due to the continuous nature of data streams, finding evolving clusters is a challenging task. This paper proposes ISsC, which is an incremental clustering algorithm of subsequences in multiple data streams. The ISsC algorithm employs a window buffer to collect and process the continuous data. Clusters found in previous windows are kept in a global List. Then, the List of clusters is updated incrementally by clusters found in the current without the need to recompute the clusters from the entire historical streams. If the number of cluster members (e.g., subsequences) is above a certain threshold, the cluster is deemed a frequent subsequence. Old clusters are tracked through a decay parameter and removed from the global List once this parameter is decayed to a negative value. Extensive experiments are conducted on multiple data stre ams to show the feasibility of the ISsC algorithm. (More)

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Paper citation in several formats:
Al Aghbari, Z.; Al Hamadi, A. and Baker, T. (2022). Incremental Subsequence Clustering Algorithm from Multiple Data Streams. In Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - BML; ISBN 978-989-758-559-3, SciTePress, pages 92-96. DOI: 10.5220/0010729000003101

@conference{bml22,
author={Zaher {Al Aghbari}. and Ayoub {Al Hamadi}. and Thar Baker.},
title={Incremental Subsequence Clustering Algorithm from Multiple Data Streams},
booktitle={Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - BML},
year={2022},
pages={92-96},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010729000003101},
isbn={978-989-758-559-3},
}

TY - CONF

JO - Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - BML
TI - Incremental Subsequence Clustering Algorithm from Multiple Data Streams
SN - 978-989-758-559-3
AU - Al Aghbari, Z.
AU - Al Hamadi, A.
AU - Baker, T.
PY - 2022
SP - 92
EP - 96
DO - 10.5220/0010729000003101
PB - SciTePress

- Science and Technology Publications, Lda.
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