East European Conference on Advances in Databases and Information Systems

ADBIS 2015: New Trends in Databases and Information Systems pp 175-185 | Cite as

CLUS: Parallel Subspace Clustering Algorithm on Spark

  • Bo ZhuEmail author
  • Alexandru Mara
  • Alberto Mozo
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 539)


Subspace clustering techniques were proposed to discover hidden clusters that only exist in certain subsets of the full feature spaces. However, the time complexity of such algorithms is at most exponential with respect to the dimensionality of the dataset. In addition, datasets are generally too large to fit in a single machine under the current big data scenarios. The extremely high computational complexity, which results in poor scalability with respect to both size and dimensionality of these datasets, give us strong motivations to propose a parallelized subspace clustering algorithm able to handle large high dimensional data. To the best of our knowledge, there are no other parallel subspace clustering algorithms that run on top of new generation big data distributed platforms such as MapReduce and Spark. In this paper we introduce CLUS: a novel parallel solution of subspace clustering based on SUBCLU algorithm. CLUS uses a new dynamic data partitioning method specifically designed to continuously optimize the varying size and content of required data for each iteration in order to fully take advantage of Spark’s in-memory primitives. This method minimizes communication cost between nodes, maximizes their CPU usage, and balances the load among them. Consequently the execution time is significantly reduced. Finally, we conduct several experiments with a series of real and synthetic datasets to demonstrate the scalability, accuracy and the nearly linear speedup with respect to number of nodes of the implementation.


Subspace Parallel Clustering Spark Big data 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Universidad Politécnica de MadridMadridSpain

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