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Parallel Subspace Clustering Using Multi-core and Many-core Architectures

  • Finding clusters in high dimensional data is a challenging research problem. Subspace clustering algorithms aim to find clusters in all possible subspaces of the dataset where, a subspace is the subset of dimensions of the data. But exponential increase in the number of subspaces with the dimensionality of data renders most of the algorithms inefficient as well as ineffective. Moreover, theseFinding clusters in high dimensional data is a challenging research problem. Subspace clustering algorithms aim to find clusters in all possible subspaces of the dataset where, a subspace is the subset of dimensions of the data. But exponential increase in the number of subspaces with the dimensionality of data renders most of the algorithms inefficient as well as ineffective. Moreover, these algorithms have ingrained data dependency in the clustering process, thus, parallelization becomes difficult and inefficient. SUBSCALE is a recent subspace clustering algorithm which is scalable with the dimensions and contains independent processing steps which can be exploited through parallelism. In this paper, we aim to leverage, firstly, the computational power of widely available multi-core processors to improve the runtime performance of the SUBSCALE algorithm. The experimental evaluation has shown linear speedup. Secondly, we are developing an approach using graphics processing units (GPUs) for fine-grained data parallelism to accelerate the computation further. First tests of the GPU implementation show very promising results.show moreshow less

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Metadaten
Document Type:Conference Proceeding
Conference Type:Konferenzartikel
Zitierlink: https://opus.hs-offenburg.de/2500
Bibliografische Angaben
Title (English):Parallel Subspace Clustering Using Multi-core and Many-core Architectures
Conference:ADBIS 2017: European Conference on Advances in Databases and Information Systems, September 24-27, 2017, Nicosia, Cyprus
Author:Tobias LauerStaff MemberGND, Amitava Datta, Amardeep Kaur, Sami Chabbouh
Year of Publication:2017
Place of publication:Cham
Publisher:Springer
First Page:213
Last Page:223
Parent Title (English):New Trends in Databases and Information Systems : ADBIS 2017 Short Papers and Workshops
Editor:Mārīte Kirikova, Kjetil Nørvåg, George Papadopoulos, Johann Gamper, Robert Wrembel, Jérôme Darmont, Stefano Rizzi
ISBN:978-3-319-67161-1 (Softcover)
ISBN:978-3-319-67162-8 (eBook)
ISSN:1865-0929
ISSN:1865-0937 (E-ISSN)
DOI:https://doi.org/10.1007/978-3-319-67162-8_21
URL:https://link.springer.com/chapter/10.1007/978-3-319-67162-8_21
Language:English
Inhaltliche Informationen
Institutes:Fakultät Elektrotechnik und Informationstechnik (E+I) (bis 03/2019)
Institutes:Bibliografie
Tag:Data mining; GPU computing; Many-core architectures; Multi-core architectures; Subspace clustering
Formale Angaben
Open Access: Closed Access 
Licence (German):License LogoUrheberrechtlich geschützt