<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></abstract>

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.

Exploiting multi–core and many–core parallelism for subspace clustering

School of Computer Science and Software Engineering

Department of Electrical Engineering and Information Technology

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Finding clusters in high dimensional data is a challenging research problem. Subspace clustering algorithms aim to find clusters in all possible subspaces of...

Exploiting multi–core and many–core parallelism for subspace clustering

Data publikacji: 29 mar 2019

Zakres stron: 81 - 91

Otrzymano: 10 lut 2018

Przyjęty: 16 wrz 2018

DOI: https://doi.org/10.2478/amcs-2019-0006

Słowa kluczowe
data mining, subspace clustering, multi-core, many-core, GPU computing

© 2019 Amitava Datta et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Exploiting multi–core and many–core parallelism for subspace clustering

Data publikacji: 29 mar 2019

Zakres stron: 81 - 91

Otrzymano: 10 lut 2018

Przyjęty: 16 wrz 2018

DOI: https://doi.org/10.2478/amcs-2019-0006

Słowa kluczowedata mining, subspace clustering, multi-core, many-core, GPU computing

© 2019 Amitava Datta et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
data mining, subspace clustering, multi-core, many-core, GPU computing