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Paper details
Number 1 - March 2019
Volume 29 - 2019
Exploiting multi-core and many-core parallelism for subspace clustering
Amitava Datta, Amardeep Kaur, Tobias Lauer, Sami Chabbouh
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.
Keywords
data mining, subspace clustering, multi-core, many-core, GPU computing