[Aggarwal, C.C. and Reddy, C.K. (2013). Data Clustering: Algorithms and Applications, 1st Edn., Chapman & Hall/CRC.]Search in Google Scholar
[Aggarwal, C.C., Wolf, J.L., Yu, P.S., Procopiuc, C. and Park, J.S. (1999). Fast algorithms for projected clustering, SIGMOD Record28(2): 61–72.10.1145/304181.304188]Search in Google Scholar
[Agrawal, R., Gehrke, J., Gunopulos, D. and Raghavan, P. (1998). Automatic subspace clustering of high dimensional data for data mining applications, ACM SIGMOD International Conference on Management of Data, Seattle, WA, USA, Vol. 27, pp. 94–105.10.1145/276305.276314]Search in Google Scholar
[Alcantara, D.A.F. (2011). Efficient Hash Tables on the GPU, PhD thesis, University of California Davis, Davis, CA.]Search in Google Scholar
[Anderson, S.E. (2018). Bit Twiddling Hacks–compute the lexicographically next bit permutation, http://graphics.stanford.edu/~seander/bithacks.html#NextBitPermutation.]Search in Google Scholar
[Berkhin, P. (2006). A survey of clustering data mining techniques, in J. Kogan et al. (Eds.), Grouping Multidimensional Data, Springer, Berlin/Heidelberg, pp. 25–71.10.1007/3-540-28349-8_2]Search in Google Scholar
[Cheng, C.-H., Fu, A.W. and Zhang, Y. (1999). Entropy-based subspace clustering for mining numerical data, 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, pp. 84–93.10.1145/312129.312199]Search in Google Scholar
[Dagum, L. and Menon, R. (1998). OpenMP: An industry standard API for shared-memory programming, IEEE Computational Science Engineering5(1): 46–55.10.1109/99.660313]Search in Google Scholar
[Datta, A., Kaur, A., Lauer, T. and Chabbouh, S. (2017). Parallel subspace clustering using multi-core and many-core architectures, in M. Kirikova et al. (Eds.), New Trends in Databases and Information Systems, Springer International Publishing, Cham, pp. 213–223.10.1007/978-3-319-67162-8_21]Search in Google Scholar
[Elhamifar, E. and Vidal, R. (2013). Sparse subspace clustering: Algorithm, theory, and applications, IEEE Transactions on Pattern Analysis and Machine Intelligence35(11): 2765–2781.10.1109/TPAMI.2013.5724051734]Search in Google Scholar
[Ester, M., Kriegel, H.-P., Sander, J. and Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise, International Conference on Knowledge Discovery and Data Mining, Portland, OR, USA, pp. 226–231.]Search in Google Scholar
[Fan, J., Han, F. and Liu, H. (2014). Challenges of big data analysis, National Science Review1(2): 293–314.10.1093/nsr/nwt032423684725419469]Search in Google Scholar
[Fukunaga, K. (1990). Introduction to Statistical Pattern Recognition, Academic Press, San Diego, CA.10.1016/B978-0-08-047865-4.50007-7]Search in Google Scholar
[Geiger, A., Lenz, P., Stiller, C. and Urtasun, R. (2013). Vision meets robotics: The KITTI dataset, The International Journal of Robotics Research32(11): 1231–1237.10.1177/0278364913491297]Search in Google Scholar
[Google Scholar (2018). Search for ‘data clustering’, https://scholar.google.com/scholar?q=data+clustering&btnG=.]Search in Google Scholar
[Han, J., Kamber, M. and Pei, J. (2011). Data Mining: Concepts and Techniques, 3rd Edn., Morgan Kaufmann Publishers, San Francisco, CA.]Search in Google Scholar
[Harris, M., Sengupta, S. and Owens, J.D. (2007). Parallel prefix sum (scan) with CUDA, GPU Gems3(39): 851–876.]Search in Google Scholar
[Jain, A.K. and Dubes, R.C. (1988). Algorithms for Clustering Data, Prentice-Hall, Inc., Upper Saddle River, NJ.]Search in Google Scholar
[Jain, A.K., Murty, M.N. and Flynn, P.J. (1999). Data clustering: A review, ACM Computing Surveys31(3): 264–323.10.1145/331499.331504]Search in Google Scholar
[Joliffe, I.T. (2002). Principle Component Analysis, 2nd Edn., Springer, New York, NY.]Search in Google Scholar
[Jun, J., Chung, S. and McLeod, D. (2006). Subspace clustering of microarray data based on domain transformation, VLDB Workshop on Data Mining and Bioinformatics, Seoul, Korea, pp. 14–28.10.1007/11960669_3]Search in Google Scholar
[Kailing, K., Kriegel, H.-P. and Kröger, P. (2004). Density-connected subspace clustering for high-dimensional data, SIAM International Conference on Data Mining, Lake Buena Vista, FL, USA, Vol. 4, pp. 246–256.10.1137/1.9781611972740.23]Search in Google Scholar
[Kaur, A. and Datta, A. (2014). Subscale: Fast and scalable subspace clustering for high dimensional data, IEEE International Conference on Data Mining Workshop, Shenzhen, China, pp. 621–628.10.1109/ICDMW.2014.100]Search in Google Scholar
[Kaur, A. and Datta, A. (2015). A novel algorithm for fast and scalable subspace clustering of high-dimensional data, Journal of Big Data2(1): 1–24.10.1186/s40537-015-0027-y]Search in Google Scholar
[Kriegel, H.-P., Kröger, P. and Zimek, A. (2009). Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering, ACM Transactions on Knowledge Discovery from Data3(1): 1–58.10.1145/1497577.1497578]Search in Google Scholar
[Li, T., Ma, S. and Ogihara, M. (2004). Document clustering via adaptive subspace iteration, 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Sheffield, UK, pp. 218–225.10.1145/1008992.1009031]Search in Google Scholar
[Lichman, M. (2013). UCI machine learning repository, http://archive.ics.uci.edu/ml.]Search in Google Scholar
[Loughry, J., van Hemert, J. and Schoofs, L. (2000). Efficiently enumerating the subsets of a set, http://www.applied-math.org/subset.pdf.]Search in Google Scholar
[MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations, 5th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, CA, USA, Vol. 1, pp. 281–297.]Search in Google Scholar
[McCaffrey, J. (2004). Generating the MTH lexicographical element of a mathematical combination, MSDN Library, Microsoft, Redmond, WA.]Search in Google Scholar
[Murtagh, F. (1983). A survey of recent advances in hierarchical clustering algorithms, The Computer Journal26(4): 354–359.10.1093/comjnl/26.4.354]Search in Google Scholar
[Nagesh, H., Goil, S. and Choudhary, A. (2001). Adaptive grids for clustering massive data sets, 1st SIAM International Conference on Data Mining, Chicago, IL, USA, pp. 1–17.10.1137/1.9781611972719.7]Search in Google Scholar
[Nvidia CUDA (2018). CUDA parallel computing platform and programming model, http://www.nvidia.com/object/cuda_home_new.html.]Search in Google Scholar
[Parsons, L., Haque, E. and Liu, H. (2004). Subspace clustering for high dimensional data: A review, ACM SIGKDD Explorations Newsletter6(1): 90–105.10.1145/1007730.1007731]Search in Google Scholar
[Sim, K., Gopalkrishnan, V., Zimek, A. and Cong, G. (2013). A survey on enhanced subspace clustering, Data Mining and Knowledge Discovery26(2): 332–397.10.1007/s10618-012-0258-x]Search in Google Scholar
[Steinbach, M., Ertöz, L. and Kumar, V. (2004). The challenges of clustering high dimensional data, in L.T. Wille (Ed.), New Directions in Statistical Physics, Springer, Berlin/Heidelberg, pp. 273–309.10.1007/978-3-662-08968-2_16]Search in Google Scholar
[Strohm, P.T., Wittmer, S., Haberstroh, A. and Lauer, T. (2015). GPU-accelerated quantification filters for analytical queries in multidimensional databases, in N. Bassiliades et al. (Eds.), New Trends in Databases and Information Systems II, Springer, Cham, pp. 229–242.10.1007/978-3-319-10518-5_18]Search in Google Scholar
[Thalamuthu, A., Mukhopadhyay, I., Zheng, X. and Tseng, G.C. (2006). Evaluation and comparison of gene clustering methods in microarray analysis, Bioinformatics22(19): 2405–2412.10.1093/bioinformatics/btl40616882653]Search in Google Scholar
[Tierney, S., Gao, J. and Guo, Y. (2014). Subspace clustering for sequential data, IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, pp. 1019–1026.10.1109/CVPR.2014.134]Search in Google Scholar
[Xu, D. and Tian, Y. (2015). A comprehensive survey of clustering algorithms, Annals of Data Science2(2): 165–193.10.1007/s40745-015-0040-1]Search in Google Scholar
[Xu, R. and Wunsch, D. (2005). Survey of clustering algorithms, IEEE Transactions on Neural Networks16(3): 645–678.10.1109/TNN.2005.84514115940994]Search in Google Scholar
[Zhu, B., Mara, A. and Mozo, A. (2015). CLUS: Parallel subspace clustering algorithm on spark, in T. Morzy et al. (Eds.), New Trends in Databases and Information Systems, Communications in Computer and Information Science, Vol. 539, Springer International Publishing, Cham, pp. 175–185.10.1007/978-3-319-23201-0_20]Search in Google Scholar
[Zhu, J., Liao, S., Lei, Z., Yi, D. and Li, S.Z. (2013). Pedestrian attribute classification in surveillance: Database and evaluation, ICCV Workshop on Large-Scale Video Search and Mining (LSVSM’13), Sydney, Australia, pp. 331–338.10.1109/ICCVW.2013.51]Search in Google Scholar