Abstract
Missing data may be one of the biggest problems hindering modern research science. It occurs frequently, for various reasons, and slows down crucial data analytics required to answer important questions related to global issues like climate change and water management. The modern answer to this problem of missing data is data imputation. Specifically, data imputation with advanced machine learning techniques. Unfortunately, an approach with demonstrable success for accurate imputation, Fuzzy K-Means Clustering, is famously slow compared to other algorithms. This paper aims to remedy this foible of such a promising method by proposing a Robust and Sparse Fuzzy K-Means algorithm that operates on multiple GPUs. We demonstrate the effectiveness of our implementation with multiple experiments, clustering real environmental sensor data. These experiments show that the our improved multi-GPU implementation is significantly faster than sequential implementations with 185 times speedup over 8 GPUs. Experiments also indicated greater than 300x increase in throughput with 8 GPUs and 95% efficiency with two GPUs compared to one.
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References
NRDC: Nevada research data center [Online]. Available: http://sensor.nevada.edu/NRDC/ pp. 1–6 (2018). Last accessed 12 Dec 2018
Schmitt, P., Mandel, J., Guedj, M.: A comparison of six methods for missing data imputation. J. Biometrics Biostat. 6, 1–6 (2015). https://www.omicsonline.org/open-access/a-comparison-of-six-methods-for-missing-data-imputation-2155-6180-1000224.php?aid=54590
Soley-Bori, M.: Dealing with missing data: key assumptions and methods for applied analysis. Department of Health Policy and Management, School of Public Health, Boston University, Technical Report 4, May 2013
Beretta, L., Santaniello, A.: Nearest neighbor imputation algorithms: a critical evaluation. BMC Med. Inform. Decis. Mak. 16(3), 74 (2016)
Dunn, J.C.: A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters. J. Cybern. 3(3), 32–57 (1973)
Banerjee, T., Keller, J.M., Skubic, M., Stone, E.: Day or night activity recognition from video using fuzzy clustering techniques. IEEE Trans. Fuzzy Syst. 22(3), 483–493 (2014)
Valafar, F.: Pattern recognition techniques in microarray data analysis: a survey. Ann. N. Y. Acad. Sci. 980(1), 41–64 (2002)
Li, D., Deogun, J., Spaulding, W., Shuart, B.: Towards missing data imputation: a study of fuzzy k-means clustering method. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds.) Rough Sets and Current Trends in Computing, pp. 573–579. Springer, Berlin/Heidelberg (2004)
Liao, Z., Lu, X., Yang, T., Wang, H.: Missing data imputation: a fuzzy k-means clustering algorithm over sliding window. In: Sixth International Conference on Fuzzy Systems and Knowledge Discovery, 2009. FSKD’09, vol. 3, pp. 133–137. IEEE (2008)
Tang, J., Zhang, G., Wang, Y., Wang, H., Liu, F.: A hybrid approach to integrate fuzzy c-means based imputation method with genetic algorithm for missing traffic volume data estimation. Transp. Res. Part C: Emerg. Technol. 51, 29–40 (2015)
Azim, S., Aggarwal, S.: Hybrid model for data imputation: using fuzzy c means and multi layer perceptron. In: 2014 IEEE International Advance Computing Conference (IACC), pp. 1281–1285, Feb 2014
Shalom, S.A.A., Dash, M., Tue, M.: Efficient k-means clustering using accelerated graphics processors. In: Song, I.-Y., Eder, J., Nguyen, T.M. (eds.) Data Warehousing and Knowledge Discovery, pp. 166–175. Springer, Berlin/Heidelberg (2008)
Shalom, S.A.A., Dash, M., Tue, M.: Graphics hardware based efficient and scalable fuzzy c-means clustering. In: Proceedings of the 7th Australasian Data Mining Conference – Volume 87, AusDM’08, Darlinghurst, pp. 179–186. Australian Computer Society, Inc. (2008)
Al-Ayyoub, M., Abu-Dalo, A.M., Jararweh, Y., Jarrah, M., Sa’d, M.A.: A gpu-based implementations of the fuzzy c-means algorithms for medical image segmentation. J. Supercomput. 71(8), 3149–3162 (2015)
Xu, J., Han, J., Xiong, K., Nie, F.: Robust and sparse fuzzy k-means clustering. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI’16, pp. 2224–2230. AAAI Press (2016)
Huang, J., Nie, F., Huang, H.: A new simplex sparse learning model to measure data similarity for clustering. In: Proceedings of the 24th International Conference on Artificial Intelligence, IJCAI’15, pp. 3569–3575. AAAI Press (2015)
Domahidi, A., Chu, E., Boyd, S.: ECOS: an SOCP solver for embedded systems. In: European Control Conference (ECC), pp. 3071–3076 (2013)
Diamond, S., Boyd, S.: CVXPY: a Python-embedded modeling language for convex optimization. J. Mach. Learn. Res. 17(83), 1–5 (2016)
Mattingley, J., Boyd, S.: Cvxgen: a code generator for embedded convex optimization. Optim. Eng. 13(1), 1–27 (2012)
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This material is based upon work supported by the National Science Foundation under grant number IIA1301726. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
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Scully-Allison, C., Wu, R., Dascalu, S.M., Barford, L., Harris, F.C. (2019). Data Imputation with an Improved Robust and Sparse Fuzzy K-Means Algorithm. In: Latifi, S. (eds) 16th International Conference on Information Technology-New Generations (ITNG 2019). Advances in Intelligent Systems and Computing, vol 800. Springer, Cham. https://doi.org/10.1007/978-3-030-14070-0_41
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DOI: https://doi.org/10.1007/978-3-030-14070-0_41
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