Incremental Density-based Clustering on Multicore Processors

Thai Son Mai, Jon Jacobsen, Sihem Amer-Yahia, Ivor Spence, Ira Assent, Nhat Phuong Tran, Quoc Viet Hung Nguyen

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The density-based clustering algorithm is a fundamental data clustering technique with many real-world applications. However, when the database is frequently changed, how to effectively update clustering results rather than reclustering from scratch remains a challenging task. In this work, we introduce IncAnyDBC, a unique parallel incremental data clustering approach to deal with this problem. First, IncAnyDBC can process changes in bulks rather than batches like state-of-the-art methods for reducing update overheads. Second, it keeps an underlying cluster structure called the object node graph during the clustering process and uses it as a basis for incrementally updating clusters wrt. inserted or deleted objects in the database by propagating changes around affected nodes only. In additional, IncAnyDBC actively and iteratively examines the graph and chooses only a small set of most meaningful objects to produce exact clustering results of DBSCAN or to approximate results under arbitrary time constraints. This makes it more efficient than other existing methods. Third, by processing objects in blocks, IncAnyDBC can be efficiently parallelized on multicore CPUs, thus creating a work-efficient method. It runs much faster than existing techniques using one thread while still scaling well with multiple threads. Experiments are conducted on various large real datasets for demonstrating the performance of IncAnyDBC.
Original languageEnglish
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Early online date10 Sep 2020
Publication statusEarly online date - 10 Sep 2020


  • Density-based clustering
  • Incremental clustering
  • Dynamic clustering
  • Multicore CPUs
  • Parallel processing
  • Active clustering


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