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 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.
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References
Parsons, L., Haque, E., Liu, H.: Subspace clustering for high dimensional data: a review. ACM SIGKDD Explor. Newsl. 6(1), 90–105 (2004)
Aggarwal, C.C., Reddy, C.K.: Data Clustering: Algorithms and Applications, 1st edn. Chapman & Hall/CRC, Boca Raton (2013)
Kaur, A., Datta, A.: Subscale: fast and scalable subspace clustering for high dimensional data. In: 2014 IEEE International Conference on Data Mining Workshop (ICDMW), pp. 621–628 (2014)
Kaur, A., Datta, A.: A novel algorithm for fast and scalable subspace clustering of high-dimensional data. J. Big Data 2(1), 17 (2015)
Sim, K., Gopalkrishnan, V., Zimek, A., Cong, G.: A survey on enhanced subspace clustering. Data Min. Knowl. Disc. 26(2), 332–397 (2013)
Agrawal, R., Gehrke, J., Gunopulos, D.: Automatic subspace clustering of high dimensional data for data mining applications. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 94–105 (1998)
Kailing, K., Kriegel, H.P., Kroger, P.: Density-connected subspace clustering for high-dimensional data. In: SIAM International Conference on Data Mining, pp. 246–256 (2004)
Zhu, B., Mara, A., Mozo, A.: CLUS: parallel subspace clustering algorithm on spark. In: Morzy, T., Valduriez, P., Bellatreche, L. (eds.) ADBIS 2015. CCIS, vol. 539, pp. 175–185. Springer, Cham (2015). doi:10.1007/978-3-319-23201-0_20
Dagum, L., Menon, R.: OpenMP: an industry standard API for shared-memory programming. IEEE Comput. Sci. Eng. 5, 46–55 (1998)
Bache, K., Lichman, M.: UCI Machine Learning Repository (2013)
Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: the KITTI dataset. Int. J. Rob. Res. 32(11), 1231–1237 (2013)
Zhu, J., Liao, S., Lei, Z., Yi, D., Li, S.Z.: Pedestrian attribute classification in surveillance: database and evaluation. In: ICCV Workshop on Large-Scale Video Search and Mining (LSVSM 2013), Sydney (2013)
Nvidia: CUDA home page. http://www.nvidia.com/object/cuda_home_new.html. Accessed 26 May 2017
Loughry, J., van Hemert, J., Schoofs, L.: Efficiently enumerating the subsets of a set (2000). applied-math.org/subset.pdf
McCaffrey, J.: Generating the mth lexicographical element of a mathematical combination. MSDN Library (2004)
Anderson, S.E.: Bit Twiddling Hacks compute the lexicographically next bit permutation. http://graphics.stanford.edu/~seander/bithacks.html#NextBitPermutation. Accessed 26 May 2017
Harris, M., Sengupta, S., Owens, J.D.: Parallel prefix sum (scan) with CUDA. GPU gems 3(39), 851–876 (2007)
Alcantara, D.A.F.: Efficient hash tables on the GPU. Ph.D. thesis, University of California Davis (2011)
Strohm, P.T., Wittmer, S., Haberstroh, A., Lauer, T.: GPU-accelerated quantification filters for analytical queries in multidimensional databases. In: Bassiliades, N., Ivanovic, M., Kon-Popovska, M., Manolopoulos, Y., Palpanas, T., Trajcevski, G., Vakali, A. (eds.) New Trends in Database and Information Systems II. AISC, vol. 312, pp. 229–242. Springer, Cham (2015). doi:10.1007/978-3-319-10518-5_18
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Datta, A., Kaur, A., Lauer, T., Chabbouh, S. (2017). Parallel Subspace Clustering Using Multi-core and Many-core Architectures. In: Kirikova, M., et al. New Trends in Databases and Information Systems. ADBIS 2017. Communications in Computer and Information Science, vol 767. Springer, Cham. https://doi.org/10.1007/978-3-319-67162-8_21
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DOI: https://doi.org/10.1007/978-3-319-67162-8_21
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