Clustering data over time using kernel spectral clustering with memory

Rocco Langone, RaghvenPhDa Mall, Johan A.K. Suykens

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

This paper discusses the problem of clustering data changing over time, a research domain that is attracting increasing attention due to the increased availability of streaming data in the Web 2.0 era. In the analysis conducted throughout the paper we make use of the kernel spectral clustering with memory (MKSC) algorithm, which is developed in a constrained optimization setting. Since the objective function of the MKSC model is designed to explicitly incorporate temporal smoothness, the algorithm belongs to the family of evolutionary clustering methods. Experiments over a number of real and synthetic datasets provide very interesting insights in the dynamics of the clusters evolution. Specifically, MKSC is able to handle objects leaving and entering over time, and recognize events like continuing, shrinking, growing, splitting, merging, dissolving and forming of clusters. Moreover, we discover how one of the regularization constants of the MKSC model, referred as the smoothness parameter, can be used as a change indicator measure. Finally, some possible visualizations of the cluster dynamics are proposed.

Original languageEnglish
Title of host publicationIEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIDM 2014
Subtitle of host publication2014 IEEE Symposium on Computational Intelligence and Data Mining, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-8
Number of pages8
ISBN (Electronic)9781479945191
DOIs
Publication statusPublished - 1 Jan 2015
Externally publishedYes
Event5th IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2014 - Orlando, United States
Duration: 9 Dec 201412 Dec 2014

Other

Other5th IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2014
CountryUnited States
CityOrlando
Period9/12/1412/12/14

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ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems
  • Signal Processing
  • Software

Cite this

Langone, R., Mall, R., & Suykens, J. A. K. (2015). Clustering data over time using kernel spectral clustering with memory. In IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIDM 2014: 2014 IEEE Symposium on Computational Intelligence and Data Mining, Proceedings (pp. 1-8). [7008141] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CIDM.2014.7008141