Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Modeling Evolving User Behavior via Sequential Clustering
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0001-7199-8080
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we address the problem of modeling the evolution of clusters over time by applying sequential clustering. We propose a sequential partitioning algorithm that can be applied for grouping distinct snapshots of streaming data so that a clustering model is built on each data snapshot. The algorithm is initialized by a clustering solution built on available historical data. Then a new clustering solution is generated on each data snapshot by applying a partitioning algorithm seeded with the centroids of the clustering model obtained at the previous time interval. At each step the algorithm also conducts model adapting operations in order to reflect the evolution in the clustering structure. In that way, it enables to deal with both incremental and dynamic aspects of modeling evolving behavior problems. In addition, the proposed approach is able to trace back evolution through the detection of clusters' transitions, such as splits and merges. We have illustrated and initially evaluated our ideas on household electricity consumption data. The results have shown that the proposed sequential clustering algorithm is robust to modeling evolving behavior by being enable to mine changes and update the model, respectively.

Place, publisher, year, edition, pages
Springer, 2019.
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-18666OAI: oai:DiVA.org:bth-18666DiVA, id: diva2:1352327
Conference
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECMLPKDD, Würzburg,16th to the 20th of September
Available from: 2019-09-18 Created: 2019-09-18 Last updated: 2019-10-17Bibliographically approved
In thesis
1.
The record could not be found. The reason may be that the record is no longer available or you may have typed in a wrong id in the address field.

Open Access in DiVA

No full text in DiVA

Search in DiVA

By author/editor
Boeva, VeselkaNordahl, Christian
By organisation
Department of Computer Science
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 96 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf