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MultiStream EvolveCluster
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0001-7199-8080
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0003-3128-191x
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0001-9947-1088
2023 (English)In: The 36th Canadian Conference on Artificial Intelligence, 2023Conference paper, Published paper (Refereed)
Abstract [en]

This paper proposes a novel multi-stream clustering algorithm, MultiStream EvolveCluster (MS-EC), that can be used for continuous and distributed monitoring and analysis ofevolving time series phenomena. It can maintain evolving clustering solutions separatelyfor each stream/view and consensus clustering solutions reflecting evolving interrelationsamong the streams. Each stream behavior can be analyzed by different clustering techniques using a distance measure and data granularity that is specially selected for it. Theproperties of the MultiStream EvolveCluster algorithm are studied and evaluated withrespect to different consensus clustering techniques, distance measures, and cluster evaluation measures in synthetic and real-world smart building datasets. Our evaluation resultsshow a stable algorithm performance in synthetic data scenarios. In the case of real-worlddata, the algorithm behavior demonstrates sensitivity to the individual streams’ data quality and the used consensus clustering technique.

Place, publisher, year, edition, pages
2023.
Keywords [en]
evolve clustering, data stream mining, consensus clustering
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-25534OAI: oai:DiVA.org:bth-25534DiVA, id: diva2:1808809
Conference
The 36th Canadian Conference on Artificial Intelligence, Montreal, 5-9 June 2023
Available from: 2023-11-01 Created: 2023-11-01 Last updated: 2023-11-03Bibliographically approved
In thesis
1. Data Stream Mining and Analysis: Clustering Evolving Data
Open this publication in new window or tab >>Data Stream Mining and Analysis: Clustering Evolving Data
2024 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Streaming data is becoming more prevalent in our society every day. With the increasing use of technologies such as the Internet of Things (IoT) and 5G networks, the number of possible data sources steadily increases. Therefore, there is a need to develop algorithms that can handle the massive amount of data we now generate.

Data mining is an area of research where data is mined to gain an understanding of data and its underlying structure. When we move to streaming data, and the corresponding sub-domain data stream mining, restrictions are imposed on the algorithms that can be used. Data streams are possibly endless, and their instances arrive rapidly, can often only be processed once or a few times, and often evolve as the data is generated over time.

This thesis explores data-driven techniques to model and analyze evolving data streams. We focus on slower data streams where incremental updates are not necessary, and the interest lies in analyzing its behavior over longer time periods. We aim to evaluate existing and develop novel algorithms and techniques suitable for analyzing these types of data streams. We use both supervised and unsupervised learning methods to model the user/system behaviors, and the methods and algorithms are evaluated on various datasets.

Specifically, we investigate regression and clustering algorithms to mine streaming data for user/system behavior patterns. We also design an algorithm capable of modeling user/system behavior in a single evolving data stream, which is easy to use and capitalizes on prior knowledge from the history of the stream. Furthermore, we design a clustering algorithm that takes advantage of multiple data streams, where each stream represents a part of the entire system, to model various aspects of the user/system behavior. Finally, we review the current state-of-the-art methods for evaluating data stream clustering algorithms and identify aspects that should be considered for the future.

Place, publisher, year, edition, pages
Karlskrona: Blekinge Tekniska Högskola, 2024. p. 231
Series
Blekinge Institute of Technology Doctoral Dissertation Series, ISSN 1653-2090 ; 1
Keywords
Data Stream Mining, Clustering, Data Streams, Data Mining
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:bth-25539 (URN)978-91-7295-472-4 (ISBN)
Public defence
2024-01-24, Karlskrona, 09:00 (English)
Opponent
Supervisors
Available from: 2023-11-17 Created: 2023-11-03 Last updated: 2023-12-12Bibliographically approved

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Nordahl, ChristianBoeva, VeselkaGrahn, Håkan

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