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Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXIX pp 169–199Cite as

Interactive Exploration of Subspace Clusters on Multicore Processors

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Part of the book series: Lecture Notes in Computer Science ((TLDKS,volume 11310))

Abstract

The PreDeCon clustering algorithm finds arbitrarily shaped clusters in high-dimensional feature spaces, which remains an active research topic with many potential applications. However, it suffers from poor runtime performance, as well as a lack of user interaction. Our new method AnyPDC introduces a novel approach to cope with these problems by casting PreDeCon into an anytime algorithm. In this anytime scheme, it quickly produces an approximate result and iteratively refines it toward the result of PreDeCon at the end. AnyPDC not only significantly speeds up PreDeCon clustering but also allows users to interact with the algorithm during its execution. Moreover, by maintaining an underlying cluster structure consisting of so-called primitive clusters and by block processing of neighborhood queries, AnyPDC can be efficiently executed in parallel on shared memory architectures such as multi-core processors. Experiments on large real world datasets show that AnyPDC achieves high quality approximate results early on, leading to orders of magnitude speedup compared to PreDeCon. Moreover, while anytime techniques are usually slower than batch ones, the algorithmic solution in AnyPDC is actually faster than PreDeCon even if run to the end. AnyPDC also scales well with the number of threads on multi-cores CPUs.

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Notes

  1. 1.

    http://openmp.org/wp/.

  2. 2.

    http://www.cs.ucr.edu/~eamonn/time_series_data/.

  3. 3.

    http://www.bbci.de/competition/iii/.

  4. 4.

    http://www.cru.uea.ac.uk/data/.

  5. 5.

    Since Ideal ignores the cluster expansion process of PreDeCon, its runtime is obviously lower than that of PreDeCon itself.

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Acknowledgments

We special thank to anonymous reviewers for their helpful comments. Part of this research was funded by a Villum postdoc fellowship, Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 102.05-2015.10 and the CDP Life Project.

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Authors and Affiliations

  1. Bach Khoa University (BKU), Ho Chi Minh City, Vietnam

    The Hai Pham

  2. Aarhus University, Aarhus, Denmark

    Jesper Kristensen, Son T. Mai, Ira Assent & Jon Jacobsen

  3. University of Grenoble Alpes, Grenoble, France

    Son T. Mai

  4. Ho Chi Minh University of Technology (HUTECH), Ho Chi Minh City, Vietnam

    Bay Vo

  5. University of Transport, Ho Chi Minh City, Vietnam

    Son T. Mai & Anh Le

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  1. The Hai Pham
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Correspondence to Son T. Mai .

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Editors and Affiliations

  1. IRIT, Paul Sabatier University, Toulouse, France

    Abdelkader Hameurlain

  2. FAW, University of Linz, Linz, Austria

    Roland Wagner

  3. IUT, University Lyon 1, Villeurbanne Cedex, France

    Djamal Benslimane

  4. University of Milan, Crema, Italy

    Ernesto Damiani

  5. University of Michigan-Dearborn, Dearborn, MI, USA

    William I. Grosky

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Pham, T.H. et al. (2018). Interactive Exploration of Subspace Clusters on Multicore Processors. In: Hameurlain, A., Wagner, R., Benslimane, D., Damiani, E., Grosky, W. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXIX. Lecture Notes in Computer Science(), vol 11310. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-58415-6_6

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