Scalable Active Temporal Constrained Clustering

Thai Son Mai, Sihem Amer-Yahia, Ahlame Douzal Chouakria

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

3 Citations (Scopus)
33 Downloads (Pure)

Abstract

We introduce a novel interactive framework to handle both instance-level and temporal smoothness constraints for clustering large temporal data. It consists of a constrained clustering algorithm which optimizes the clustering quality, constraint violation and the historical cost between consecutive data snapshots. At the center of our framework is a simple yet effective active learning technique for iteratively selecting the most informative pairs of objects to query users about, and updating the clustering with new constraints. Those constraints are then propagated inside each snapshot and between snapshots via constraint inheritance and propagation to further enhance the results. Experiments show better or comparable clustering results than state-of-the-art techniques as well as high scalability for large datasets.
Original languageEnglish
Title of host publicationInternational Conference on Extending Database Technology (EDBT): Proceedings
PublisherOpen Proceedings
Pages449-452
Number of pages4
DOIs
Publication statusPublished - 29 Mar 2018
Externally publishedYes

Keywords

  • Semi-supervised clustering
  • active learning
  • interactive clustering
  • incremental clustering
  • temporal clustering

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