Lagrangian constrained clustering

Mohadeseh Ganji, James Bailey, Peter J. Stuckey

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

6 Citations (Scopus)

Abstract

Incorporating background knowledge in clustering problems has attracted wide interest. This knowledge can be represented as pairwise instance-level constraints. Existing techniques approach satisfaction of such constraints from a soft (discretionary) perspective, yet there exist scenarios for constrained clustering where satisfying as many constraints as possible. We present a new Lagrangian Constrained Clustering framework (LCC) for clustering in the presence of pairwise constraints which gives high priority to satisfying constraints. LCC is an iterative optimization procedure which incorporates dynamic penalties for violated constraints. Experiments show that LCC can outperform existing constrained clustering algorithms in scenarios which satisfying as many constraints as possible.

Original languageEnglish
Title of host publicationProceedings of the 2016 SIAM International Conference on Data Mining
EditorsSanjay Chawla Venkatasubramanian, Wagner Meira
Place of PublicationPhiladelphia PA USA
PublisherSociety for Industrial & Applied Mathematics (SIAM)
Pages288-296
Number of pages9
ISBN (Electronic)9781510828117
DOIs
Publication statusPublished - 2016
Externally publishedYes
EventSIAM International Conference on Data Mining 2016 - Hilton Miami Downtown, Miami, United States of America
Duration: 5 May 20167 May 2016
Conference number: 16th
http://www.siam.org/meetings/sdm16/

Publication series

Name16th SIAM International Conference on Data Mining 2016, SDM 2016

Conference

ConferenceSIAM International Conference on Data Mining 2016
Abbreviated titleSDM 2016
CountryUnited States of America
CityMiami
Period5/05/167/05/16
Internet address

Keywords

  • Constrained clustering
  • Lagrangian multipliers method
  • Semi-supervised learning

Cite this

Ganji, M., Bailey, J., & Stuckey, P. J. (2016). Lagrangian constrained clustering. In S. C. Venkatasubramanian, & W. Meira (Eds.), Proceedings of the 2016 SIAM International Conference on Data Mining (pp. 288-296). (16th SIAM International Conference on Data Mining 2016, SDM 2016). Society for Industrial & Applied Mathematics (SIAM). https://doi.org/10.1137/1.9781611974348.33