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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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Author:Tobias LauerGND, Amitava Datta, Amardeep Kaur, Sami Chabbouh
Editor:Mārīte Kirikova, Kjetil Nørvåg, George Papadopoulos, Johann Gamper, Robert Wrembel, Jérôme Darmont, Stefano Rizzi
Contributor(s):Tobias Lauer
Publisher:Springer
Place of publication:Cham
Year of Publication:2017
Pagenumber:11
ISBN:978-3-319-67161-1
Language:English
Tag:Data mining; GPU computing; Many-core architectures; Multi-core architectures; Subspace clustering
Parent Title (English):New Trends in Databases and Information Systems
ISSN:1865-0929
First Page:213
Last Page:223
Document Type:Conference Proceeding
Institutes:Hochschule Offenburg / Bibliografie
Acces Right:Zugriffsbeschränkt
Release Date:2018/01/05
Licence (German):License LogoEs gilt das UrhG
URL:https://link.springer.com/chapter/10.1007/978-3-319-67162-8_21
DOI:https://doi.org/10.1007/978-3-319-67162-8_21