RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
MultiStream EvolveCluster
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.ORCID-id: 0000-0001-7199-8080
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.ORCID-id: 0000-0003-3128-191x
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.ORCID-id: 0000-0001-9947-1088
2023 (engelsk)Inngår i: The 36th Canadian Conference on Artificial Intelligence, 2023Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
2023.
Emneord [en]
evolve clustering, data stream mining, consensus clustering
HSV kategori
Identifikatorer
URN: urn:nbn:se:bth-25534OAI: oai:DiVA.org:bth-25534DiVA, id: diva2:1808809
Konferanse
The 36th Canadian Conference on Artificial Intelligence, Montreal, 5-9 June 2023
Tilgjengelig fra: 2023-11-01 Laget: 2023-11-01 Sist oppdatert: 2023-11-03bibliografisk kontrollert
Inngår i avhandling
1. Data Stream Mining and Analysis: Clustering Evolving Data
Åpne denne publikasjonen i ny fane eller vindu >>Data Stream Mining and Analysis: Clustering Evolving Data
2024 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Karlskrona: Blekinge Tekniska Högskola, 2024. s. 231
Serie
Blekinge Institute of Technology Doctoral Dissertation Series, ISSN 1653-2090 ; 1
Emneord
Data Stream Mining, Clustering, Data Streams, Data Mining
HSV kategori
Forskningsprogram
Datavetenskap
Identifikatorer
urn:nbn:se:bth-25539 (URN)978-91-7295-472-4 (ISBN)
Disputas
2024-01-24, Karlskrona, 09:00 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2023-11-17 Laget: 2023-11-03 Sist oppdatert: 2023-12-12bibliografisk kontrollert

Open Access i DiVA

fulltext(509 kB)40 nedlastinger
Filinformasjon
Fil FULLTEXT01.pdfFilstørrelse 509 kBChecksum SHA-512
ab9f5d33d4d8b2ca98232605027a5fb90ac1a08c7737d38ee804179ef2ab6b1223a99519548390dcd289620bfcbc76f7810f77832706d69d76f0835c2fa7614e
Type fulltextMimetype application/pdf

Person

Nordahl, ChristianBoeva, VeselkaGrahn, Håkan

Søk i DiVA

Av forfatter/redaktør
Nordahl, ChristianBoeva, VeselkaGrahn, Håkan
Av organisasjonen

Søk utenfor DiVA

GoogleGoogle Scholar
Totalt: 40 nedlastinger
Antall nedlastinger er summen av alle nedlastinger av alle fulltekster. Det kan for eksempel være tidligere versjoner som er ikke lenger tilgjengelige

urn-nbn

Altmetric

urn-nbn
Totalt: 233 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf