TY  - JOUR
U1  - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed)
A1  - Lauer, Tobias
A1  - Datta, Amitava
A1  - Kaur, Amardeep
A1  - Chabbouh, Sami
T1  - Exploiting Multi-Core and Many-Core Parallelism for Subspace Clustering
JF  - International Journal of Applied Mathematics and Computer Science
N2  - 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, 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.
KW  - Data Mining
KW  - Data Mining
KW  - Subspace Clustering
KW  - GPU Computing
Y1  - 2019
SN  - 2083-8492 (Online)
SS  - 2083-8492 (Online)
SN  - 1641-876X (Print)
SS  - 1641-876X (Print)
U6  - https://dx.doi.org/10.2478/amcs-2019-0006
DO  - https://dx.doi.org/10.2478/amcs-2019-0006
VL  - 29
IS  - 1
SP  - 81
EP  - 91
CY  - Zielona Góra
ER  - 
TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Lauer, Tobias
A1  - Datta, Amitava
A1  - Kaur, Amardeep
A1  - Chabbouh, Sami
ED  - Kirikova, Mārīte
ED  - Nørvåg, Kjetil
ED  - Papadopoulos, George
ED  - Gamper, Johann
ED  - Wrembel, Robert
ED  - Darmont, Jérôme
ED  - Rizzi, Stefano
T1  - Parallel Subspace Clustering Using Multi-core and Many-core Architectures
T2  - New Trends in Databases and Information Systems
N2  - 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, 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.
KW  - Data mining
KW  - Subspace clustering
KW  - Multi-core architectures
KW  - Many-core architectures
KW  - GPU computing
Y1  - 2017
UR  - https://link.springer.com/chapter/10.1007/978-3-319-67162-8_21
SN  - 1865-0929
SS  - 1865-0929
SN  - 978-3-319-67161-1
SB  - 978-3-319-67161-1
U6  - https://dx.doi.org/10.1007/978-3-319-67162-8_21
DO  - https://dx.doi.org/10.1007/978-3-319-67162-8_21
SP  - 213
EP  - 223
S1  - 11
PB  - Springer
CY  - Cham
ER  -