Scalable Active Constrained Clustering for Temporal Data

Son T. Mai, Sihem Amer-Yahia, Ahlame Douzal Chouakria, Ky T. Nguyen, Anh-Duong Nguyen

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

3 Citations (Scopus)


In this paper, 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, called CVQE+, 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, named Border, 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 data snapshot and between snapshots via two schemes, called constraint inheritance and constraint 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 Database Systems for Advanced Applications (DASFAA)
Number of pages7
Publication statusPublished - 13 May 2018

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


  • Semi-supervised clustering
  • Active learning
  • Interactive clustering
  • Incremental clustering
  • Temporal clustering

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