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EvolveCluster: an evolutionary clustering algorithm for streaming data
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
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
2022 (English)In: Evolving Systems, ISSN 1868-6478, E-ISSN 1868-6486, no 4, p. 603-623Article in journal (Refereed) Published
Abstract [en]

Data has become an integral part of our society in the past years, arriving faster and in larger quantities than before. Traditional clustering algorithms rely on the availability of entire datasets to model them correctly and efficiently. Such requirements are not possible in the data stream clustering scenario, where data arrives and needs to be analyzed continuously. This paper proposes a novel evolutionary clustering algorithm, entitled EvolveCluster, capable of modeling evolving data streams. We compare EvolveCluster against two other evolutionary clustering algorithms, PivotBiCluster and Split-Merge Evolutionary Clustering, by conducting experiments on three different datasets. Furthermore, we perform additional experiments on EvolveCluster to further evaluate its capabilities on clustering evolving data streams. Our results show that EvolveCluster manages to capture evolving data stream behaviors and adapts accordingly.

Place, publisher, year, edition, pages
SPRINGER HEIDELBERG , 2022. no 4, p. 603-623
Keywords [en]
Evolving data stream; Clustering; Data stream clustering
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-22395DOI: 10.1007/s12530-021-09408-yISI: 000717906700001Scopus ID: 2-s2.0-85119001929OAI: oai:DiVA.org:bth-22395DiVA, id: diva2:1614644
Part of project
Bigdata@BTH- Scalable resource-efficient systems for big data analytics, Knowledge Foundation
Funder
Knowledge Foundation, 20140032
Note

open access

Available from: 2021-11-26 Created: 2021-11-26 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, HåkanNetz Persson, Marie

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