Framework for novel subspace clustering using search optimization methodology

  • Authors

    • Radhika K R VTU
    • Pushpa C.N
    • Thriveni J
    • Venugopal K.R
    2018-09-26
    https://doi.org/10.14419/ijet.v7i4.15229
  • Accuracy, Elite outcomes, High-dimensional Data, Optimal Cluster, Subspace clustering,
  • Improving the yield as well as the perform of subspace clustering is one of the less-investigated topics in high-dimensional data. After reviewing existing approaches, it seriously felt that there is a need for classification of data points retrieved from a different number of subspace. The proposed study has presented a novel framework that targets to improve the accuracy of subspace clustering by addressing the problem associated with the exist of occlusion noise and dimensional complexity. An analytical approach as been proposed to design this framework with more emphasis on outlier minimization followed by obtaining optimal clusters. The technique also introduces a simple search optimization method, which is less iterative and is more productive for identifying the élite outcomes in each iterative step. The study outcome shows superior accuracy with a low rate of error when compared with the conventional approach.

     

  • References

    1. [1] Tomasev, Nenad, et al. "The role of hubness in clustering high-dimensional data." IEEE Transactions on Knowledge and Data Engineering 26.3 (2014): 739-751. https://doi.org/10.1109/TKDE.2013.25.

      [2] Yu, Zhiwen, et al. "Incremental semi-supervised clustering ensemble for high dimensional data clustering." IEEE Transactions on Knowledge and Data Engineering 28.3 (2016):701-714. https://doi.org/10.1109/TKDE.2015.2499200.

      [3] Heckel, Reinhard, and Helmut Bölcskei. "Robust subspace clustering via thresholding." IEEE Transactions on Information Theory 61.11 (2015): 6320-6342. https://doi.org/10.1109/TIT.2015.2472520.

      [4] Wu, Tong, and Waheed U. Bajwa. "Learning the nonlinear geometry of high-dimensional data: Models and algorithms." IEEE transactions on signal processing 63.23 (2015): 6229-6244. https://doi.org/10.1109/TSP.2015.2469637.

      [5] Yuan, Xiaoru, et al. "Dimension projection matrix/tree: Interactive subspace visual exploration and analysis of high dimensional data." IEEE Transactions on Visualization and Computer Graphics 19.12 (2013): 2625-2633. https://doi.org/10.1109/TVCG.2013.150.

      [6] Bouveyron, Charles, and Camille Brunet-Saumard. "Model-based clustering of high-dimensional data: A review." Computational Statistics & Data Analysis 71 (2014): 52-78. https://doi.org/10.1016/j.csda.2012.12.008.

      [7] Tian, Jinyu, et al. "Learning the Distribution Preserving Semantic Subspace for Clustering." IEEE Transactions on Image Processing 26.12 (2017): 5950-5965. https://doi.org/10.1109/TIP.2017.2748885.

      [8] Elhamifar, Ehsan, and Rene Vidal. "Sparse subspace clustering: Algorithm, theory, and applications." IEEE transactions on pattern analysis and machine intelligence 35.11 (2013): 2765-2781. https://doi.org/10.1109/TPAMI.2013.57.

      [9] He, Ran, et al. "Robust subspace clustering with complex noise." IEEE Transactions on Image Processing 24.11 (2015): 4001-4013. https://doi.org/10.1109/TIP.2015.2456504.

      [10] Yu, Zhiwen, et al. "Adaptive ensembling of semi-supervised clustering solutions." IEEE Transactions on Knowledge and Data Engineering (2017). https://doi.org/10.1109/TKDE.2017.2695615.

      [11] Li, Chun-Guang, and René Vidal. "A Structured Sparse Plus Structured Low-Rank Framework for Subspace Clustering and Completion." IEEE Trans. Signal Processing 64.24 (2016): 6557-6570. https://doi.org/10.1109/TSP.2016.2613070.

      [12] Kim, Eunwoo, Minsik Lee, and Songhwai Oh. "Robust Elastic-Net Subspace Representation." IEEE Transactions on Image Processing 25.9 (2016): 4245-4259.

      [13] Tang, Xiaoxin, et al. "Scalable multicore k-nn search via subspace clustering for filtering." IEEE Transactions on Parallel and Distributed Systems 26.12 (2015): 3449-3460. https://doi.org/10.1109/TPDS.2014.2372755.

      [14] Khachatryan, Andranik, et al. "Improving accuracy and robustness of self-tuning histograms by subspace clustering." IEEE Transactions on Knowledge and Data Engineering 27.9 (2015): 2377-2389. https://doi.org/10.1109/TKDE.2015.2416725.

      [15] Peng, Xi, Lei Zhang, and Zhang Yi. "Inductive sparse subspace clustering." Electronics Letters 49.19 (2013): 1222-1224. https://doi.org/10.1049/el.2013.1789.

      [16] Jing, Liping, Michael K. Ng, and Tieyong Zeng. "Dictionary learning-based subspace structure identification in spectral clustering." IEEE transactions on neural networks and learning systems 24.8 (2013): 1188-1199. https://doi.org/10.1109/TNNLS.2013.2253123.

      [17] Chen, Lifei, Qingshan Jiang, and Shengrui Wang. "Model-based method for projective clustering." IEEE Transactions on Knowledge and Data Engineering 24.7 (2012): 1291-1305. https://doi.org/10.1109/TKDE.2010.256.

      [18] Aldroubi, Akram, and Ali Sekmen. "Nearness to local subspace algorithm for subspace and motion segmentation." IEEE Signal Processing Letters 19.10 (2012): 704-707. https://doi.org/10.1109/LSP.2012.2214211.

      [19] Hou, Chenping, et al. "Discriminative embedded clustering: A framework for grouping high-dimensional data." IEEE transactions on neural networks and learning systems 26.6 (2015): 1287-1299. https://doi.org/10.1109/TNNLS.2014.2337335.

      [20] Muja, Marius, and David G. Lowe. "Scalable nearest neighbor algorithms for high dimensional data." IEEE Transactions on Pattern Analysis and Machine Intelligence 36.11 (2014): 2227-2240. https://doi.org/10.1109/TPAMI.2014.2321376.

      [21] Elhamifar, Ehsan, and Rene Vidal. "Sparse subspace clustering: Algorithm, theory, and applications." IEEE transactions on pattern analysis and machine intelligence 35.11 (2013): 2765-2781. https://doi.org/10.1109/TPAMI.2013.57.

      [22] Feldman, Dan, Melanie Schmidt, and Christian Sohler. "Turning big data into tiny data: Constant-size coresets for k-means, PCA, and projective clustering." Proceedings of the twenty-fourth annual ACM-SIAM symposium on discrete algorithms. Society for Industrial and Applied Mathematics, 2013. https://doi.org/10.1137/1.9781611973105.103.

      [23] Wang, Jingdong, et al. "Fast approximate k-means via cluster closures." Multimedia Data Mining and Analytics. Springer International Publishing, 2015. 373-395.

      [24] Dezeure, Ruben, et al. "High-Dimensional Inference: Confidence Intervals, $ p $-Values and R-Software hdi." Statistical science 30.4 (2015): 533-558. https://doi.org/10.1214/15-STS527.

      [25] Dyer, Eva L., Aswin C. Sankaranarayanan, and Richard G. Baraniuk. "Greedy feature selection for subspace clustering." The Journal of Machine Learning Research 14.1 (2013): 2487-2517.

      [26] Nie, Feiping, Xiaoqian Wang, and Heng Huang. "Clustering and projected clustering with adaptive neighbors." Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2014.

      [27] Song, Qinbao, Jingjie Ni, and Guangtao Wang. "A fast clustering-based feature subset selection algorithm for high-dimensional data." IEEE transactions on knowledge and data engineering 25.1 (2013): 1-14. https://doi.org/10.1109/TKDE.2011.181.

      [28] Tang, Hao, et al. "Partially supervised speaker clustering." IEEE transactions on pattern analysis and machine intelligence 34.5 (2012): 959-971. https://doi.org/10.1109/TPAMI.2011.174.

      [29] Radhika K R, Pushpa C N, Thriveni J, Venugopal K R, "Insights to Existing Techniques of Subspace Clustering in High-Dimensional Data," International Journal of Scientific and Engineering Research, 2016.

      [30] Radhika, K.R., and Pushpa, C.N. and Thriveni, J. and Venugopal, K.R. (2016) EDSC: Efficient document subspace clustering technique for high-dimensional data. In: 2016 International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT), 11-13 March 2016, Bangalore.

      [31] H. Song, W. Yang, N. Zhong and X. Xu, "Unsupervised Classification of PolSAR Imagery via Kernel Sparse Subspace Clustering," in IEEE Geoscience and Remote Sensing Letters, vol. 13, no. 10, pp. 1487-1491, Oct. 2016. https://doi.org/10.1109/LGRS.2016.2593098.

      [32] K. R. Radhika, C. N. Pushpa, J. Thriveni, K. R. Venugopal, "RMSC: Robust modeling of subspace clustering for high dimensional data", International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp.1535-1539, 2017.

  • Downloads

  • How to Cite

    K R, R., C.N, P., J, T., & K.R, V. (2018). Framework for novel subspace clustering using search optimization methodology. International Journal of Engineering & Technology, 7(4), 2710-2714. https://doi.org/10.14419/ijet.v7i4.15229