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Bipartite Split-Merge Evolutionary Clustering
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.ORCID-id: 0000-0003-3128-191x
Technical University of Sofia, BUL.
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.ORCID-id: 0000-0003-3371-5347
Vise andre og tillknytning
2019 (engelsk)Inngår i: Lect. Notes Comput. Sci., Springer , 2019, s. 204-223Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

We propose a split-merge framework for evolutionary clustering. The proposed clustering technique, entitled Split-Merge Evolutionary Clustering is supposed to be more robust to concept drift scenarios by providing the flexibility to consider at each step a portion of the data and derive clusters from it to be used subsequently to update the existing clustering solution. The proposed framework is built around the idea to model two clustering solutions as a bipartite graph, which guides the update of the existing clustering solution by merging some clusters with ones from the newly constructed clustering while others are transformed by splitting their elements among several new clusters. We have evaluated and compared the discussed evolutionary clustering technique with two other state of the art algorithms: a bipartite correlation clustering (PivotBiCluster) and an incremental evolving clustering (Dynamic split-and-merge). © Springer Nature Switzerland AG 2019.

sted, utgiver, år, opplag, sider
Springer , 2019. s. 204-223
Serie
Lecture Notes in Computer Science (LNCS), ISSN 0302-9743, E-ISSN 1611-3349
Emneord [en]
Bipartite clustering, Data mining, Dynamic clustering, Evolutionary clustering, Split-merge framework, Unsupervised learning, Artificial intelligence, Bipartite correlation clustering, Clustering solutions, Clustering techniques, State-of-the-art algorithms, Cluster analysis
HSV kategori
Identifikatorer
URN: urn:nbn:se:bth-19127DOI: 10.1007/978-3-030-37494-5_11ISI: 000722592200011Scopus ID: 2-s2.0-85077496461ISBN: 9783030374938 (tryckt)OAI: oai:DiVA.org:bth-19127DiVA, id: diva2:1387915
Konferanse
11th International Conference on Agents and Artificial Intelligence, ICAART; Prague; Czech Republic; 19 February 2019 through 21 February
Ingår i projekt
Bigdata@BTH- Scalable resource-efficient systems for big data analytics, Knowledge FoundationTilgjengelig fra: 2020-01-23 Laget: 2020-01-23 Sist oppdatert: 2023-05-10bibliografisk kontrollert
Inngår i avhandling
1. Clustering Techniques for Mining and Analysis of Evolving Data
Åpne denne publikasjonen i ny fane eller vindu >>Clustering Techniques for Mining and Analysis of Evolving Data
2021 (engelsk)Licentiatavhandling, med artikler (Annet vitenskapelig)
Abstract [en]

The amount of data generated is on rise due to increased demand for fields like IoT, smart monitoring applications, etc. Data generated through such systems have many distinct characteristics like continuous data generation, evolutionary, multi-source nature, and heterogeneity. In addition, the real-world data generated in these fields is largely unlabelled. Clustering is an unsupervised learning technique used to group, analyze and interpret unlabelled data. Conventional clustering algorithms are not suitable for dealing with data having previously mentioned characteristics due to memory and computational constraints, their inability to handle concept drift, distributed location of data. Therefore novel clustering approaches capable of analyzing and interpreting evolving and/or multi-source streaming data are needed. 

The thesis is focused on building evolutionary clustering algorithms for data that evolves over time. We have initially proposed an evolutionary clustering approach, entitled Split-Merge Clustering (Paper I), capable of continuously updating the generated clustering solution in the presence of new data. Through the progression of the work, new challenges have been studied and addressed. Namely, the Split-Merge Clustering algorithm has been enhanced in Paper II with new capabilities to deal with the challenges of multi-view data applications. A multi-view or multi-source data presents the studied phenomenon/system from different perspectives (views), and can reveal interesting knowledge that is not visible when only one view is considered and analyzed. This has motivated us to continue in this direction by designing two other novel multi-view data stream clustering algorithms. The algorithm proposed in Paper III improves the performance and interpretability of the algorithm proposed in Paper II. Paper IV introduces a minimum spanning tree based multi-view clustering algorithm capable of transferring knowledge between consecutive data chunks, and it is also enriched with a post-clustering pattern-labeling procedure. 

The proposed and studied evolutionary clustering algorithms are evaluated on various data sets. The obtained results have demonstrated the robustness of the algorithms for modeling, analyzing, and mining evolving data streams. They are able to adequately adapt single and multi-view clustering models by continuously integrating newly arriving data. 

sted, utgiver, år, opplag, sider
Karlskrona: Blekinge Tekniska Högskola, 2021
Serie
Blekinge Institute of Technology Licentiate Dissertation Series, ISSN 1650-2140 ; 2021:09
Emneord
Clustering analysis, Concept drift, Evolutionary clustering, Machine learning, Streaming data
HSV kategori
Forskningsprogram
Datavetenskap
Identifikatorer
urn:nbn:se:bth-22262 (URN)978-91-7295-432-8 (ISBN)
Presentation
2021-12-13, J1630, Blekinge Tekniska Högskola SE-371 79, Karlskrona, 13:00 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2021-11-02 Laget: 2021-11-01 Sist oppdatert: 2021-11-19bibliografisk kontrollert

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