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  • Incremental Density-based Clustering on Multicore Processors

    Author(s)
    Mai, Son
    Jacobsen, Jon
    Amer-Yahia, Sihem
    Spence, Ivor
    Tran, Phuong
    Assent, Ira
    Nguyen, Quoc Viet Hung
    Griffith University Author(s)
    Nguyen, Henry
    Year published
    2020
    Metadata
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    Abstract
    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 ...
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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.
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    Journal Title
    IEEE Transactions on Pattern Analysis and Machine Intelligence
    DOI
    https://doi.org/10.1109/tpami.2020.3023125
    Note
    This publication has been entered in Griffith Research Online as an advanced online version.
    Subject
    Artificial Intelligence and Image Processing
    Information Systems
    Electrical and Electronic Engineering
    Publication URI
    http://hdl.handle.net/10072/398181
    Collection
    • Journal articles

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