Parallel subspace clustering using multi-core and many-core architectures

Amitava Datta, Amardeep Kaur, Tobias Lauer, Sami Chabbouh

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)


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.
Original languageEnglish
Pages (from-to)213-223
Number of pages12
Journal Communications in Computer and Information Science
Publication statusPublished - Sep 2017
Event21th East European Conference on Advances in Databases and Information Systems - Nicosia, Cyprus, Greece
Duration: 24 Sep 201727 Sep 2017


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