Exploiting Multi-Core and Many-Core Parallelism for Subspace Clustering

  • 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 a subset of dimensions of the data. But the 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 a subset of dimensions of the data. But the 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, which means that 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 the computational power of widely available multi-core processors to improve the runtime performance of the SUBSCALE algorithm. The experimental evaluation shows linear speedup. Moreover, we develop 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
Creating Corporation:University of Zielona Góra
Place of publication:Zielona Góra
Year of Publication:2019
Language:English
GND Keyword:Data Mining
Tag:Data Mining; GPU Computing; Subspace Clustering
DDC classes:000 Allgemeines, Informatik, Informationswissenschaft
Parent Title (English):International Journal of Applied Mathematics and Computer Science
Volume:29
Issue:1
ISSN:2083-8492 (Online)
ISSN:1641-876X (Print)
First Page:81
Last Page:91
Document Type:Article (reviewed)
Institutes:Hochschule Offenburg / Bibliografie
Acces Right:Frei zugänglich
Release Date:2020/01/24
Licence (German):License LogoCreative Commons - CC BY-ND - Namensnennung - Keine Bearbeitungen 4.0 International
DOI:https://doi.org/10.2478/amcs-2019-0006