Efficient classification using parallel and scalable compressed model and Its application on intrusion detection

In order to achieve high efficiency of classification in intrusion detection, a compressed model is proposed in this paper which combines horizontal compression with vertical compression. OneR is utilized as horizontal com-pression for attribute reduction, and affinity propagation is employed as vertical compression to select small representative exemplars from large training data. As to be able to computationally compress the larger volume of training data with scalability, MapReduce based parallelization approach is then implemented and evaluated for each step of the model compression process abovementioned, on which common but efficient classification methods can be directly used. Experimental application study on two publicly available datasets of intrusion detection, KDD99 and CMDC2012, demonstrates that the classification using the compressed model proposed can effectively speed up the detection procedure at up to 184 times, most importantly at the cost of a minimal accuracy difference with less than 1% on average

Scalable data clustering using GPUs

The computational demands of multivariate clustering grow rapidly, and therefore processing large data sets, like those found in flow cytometry data, is very time consuming on a single CPU. Fortunately these techniques lend themselves naturally to large scale parallel processing. To address the computational demands, graphics processing units, specifically NVIDIA\u27s CUDA framework and Tesla architecture, were investigated as a low-cost, high performance solution to a number of clustering algorithms. C-means and Expectation Maximization with Gaussian mixture models were implemented using the CUDA framework. The algorithm implementations use a hybrid of CUDA, OpenMP, and MPI to scale to many GPUs on multiple nodes in a high performance computing environment. This framework is envisioned as part of a larger cloud-based workflow service where biologists can apply multiple algorithms and parameter sweeps to their data sets and quickly receive a thorough set of results that can be further analyzed by experts. Improvements over previous GPU-accelerated implementations range from 1.42x to 21x for C-means and 3.72x to 5.65x for the Gaussian mixture model on non-trivial data sets. Using a single NVIDIA GTX 260 speedups are on average 90x for C-means and 74x for Gaussians with flow cytometry files compared to optimized C code running on a single core of a modern Intel CPU. Using the TeraGrid Lincoln high performance cluster at NCSA C-means achieves 42% parallel efficiency and a CPU speedup of 4794x with 128 Tesla C1060 GPUs. The Gaussian mixture model achieves 72% parallel efficiency and a CPU speedup of 6286x

Interactive VPL-based global illumination on the GPU using fuzzy clustering

Physically-based synthesis of high quality imagery, including global illumination light transport phenomena, results in a significant workload, which makes interactive rendering a very challenging task. We propose a VPL-based ray tracing approach that runs entirely in the GPU and achieves interactive frame rates while handling global illumination light transport phenomena. This approach is based on clustering both shading points and VPLs and computing visibility only among clusters' representatives. A new massively parallel K-means clustering algorithm, enables efficient execution in the GPU. Rendering artifacts, that could result from the piecewise constant approximation of the VPLs/shading points visibility function introduced by the clustering, are smoothed away by resorting to an innovative approach based on fuzzy clustering and weighted interpolation of the visibility function. The effectiveness of the proposed approach is experimentally verified for a collection of scenes, with frame rates larger than 3 fps and up to 25 fps being demonstrated

High-throughput fuzzy clustering on heterogeneous architectures

[EN] The Internet of Things (IoT) is pushing the next economic revolution in which the main players are data and immediacy. IoT is increasingly producing large amounts of data that are now classified as "dark data'' because most are created but never analyzed. The efficient analysis of this data deluge is becoming mandatory in order to transform it into meaningful information. Among the techniques available for this purpose, clustering techniques, which classify different patterns into groups, have proven to be very useful for obtaining knowledge from the data. However, clustering algorithms are computationally hard, especially when it comes to large data sets and, therefore, they require the most powerful computing platforms on the market. In this paper, we investigate coarse and fine grain parallelization strategies in Intel and Nvidia architectures of fuzzy minimals (FM) algorithm; a fuzzy clustering technique that has shown very good results in the literature. We provide an in-depth performance analysis of the FM's main bottlenecks, reporting a speed-up factor of up to 40x compared to the sequential counterpart version.This work was partially supported by the Fundacion Seneca del Centro de Coordinacion de la Investigacion de la Region de Murcia under Project 20813/PI/18, and by Spanish Ministry of Science, Innovation and Universities under grants TIN2016-78799-P (AEI/FEDER, UE), RTI2018-096384-B-I00, RTI2018-098156-B-C53 and RTC-2017-6389-5.Cebrian, JM.; Imbernón, B.; Soto, J.; García, JM.; Cecilia-Canales, JM. (2020). High-throughput fuzzy clustering on heterogeneous architectures. Future Generation Computer Systems. 106:401-411. https://doi.org/10.1016/j.future.2020.01.022S401411106Waldrop, M. M. (2016). The chips are down for Moore’s law. Nature, 530(7589), 144-147. doi:10.1038/530144aCecilia, J. M., Timon, I., Soto, J., Santa, J., Pereniguez, F., & Munoz, A. (2018). High-Throughput Infrastructure for Advanced ITS Services: A Case Study on Air Pollution Monitoring. IEEE Transactions on Intelligent Transportation Systems, 19(7), 2246-2257. doi:10.1109/tits.2018.2816741Singh, D., & Reddy, C. K. (2014). A survey on platforms for big data analytics. Journal of Big Data, 2(1). doi:10.1186/s40537-014-0008-6Stephens, N., Biles, S., Boettcher, M., Eapen, J., Eyole, M., Gabrielli, G., … Walker, P. (2017). The ARM Scalable Vector Extension. IEEE Micro, 37(2), 26-39. doi:10.1109/mm.2017.35Wright, S. A. (2019). Performance Modeling, Benchmarking and Simulation of High Performance Computing Systems. Future Generation Computer Systems, 92, 900-902. doi:10.1016/j.future.2018.11.020Jain, A. K., Murty, M. N., & Flynn, P. J. (1999). Data clustering. ACM Computing Surveys, 31(3), 264-323. doi:10.1145/331499.331504Lee, J., Hong, B., Jung, S., & Chang, V. (2018). Clustering learning model of CCTV image pattern for producing road hazard meteorological information. Future Generation Computer Systems, 86, 1338-1350. doi:10.1016/j.future.2018.03.022Pérez-Garrido, A., Girón-Rodríguez, F., Bueno-Crespo, A., Soto, J., Pérez-Sánchez, H., & Helguera, A. M. (2017). Fuzzy clustering as rational partition method for QSAR. Chemometrics and Intelligent Laboratory Systems, 166, 1-6. doi:10.1016/j.chemolab.2017.04.006H.S. Nagesh, S. Goil, A. Choudhary, A scalable parallel subspace clustering algorithm for massive data sets, in: Proceedings 2000 International Conference on Parallel Processing, 2000, pp. 477–484.Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences, 10(2-3), 191-203. doi:10.1016/0098-3004(84)90020-7Havens, T. C., Bezdek, J. C., Leckie, C., Hall, L. O., & Palaniswami, M. (2012). Fuzzy c-Means Algorithms for Very Large Data. IEEE Transactions on Fuzzy Systems, 20(6), 1130-1146. doi:10.1109/tfuzz.2012.2201485Flores-Sintas, A., Cadenas, J., & Martin, F. (1998). A local geometrical properties application to fuzzy clustering. Fuzzy Sets and Systems, 100(1-3), 245-256. doi:10.1016/s0165-0114(97)00038-9Soto, J., Flores-Sintas, A., & Palarea-Albaladejo, J. (2008). Improving probabilities in a fuzzy clustering partition. Fuzzy Sets and Systems, 159(4), 406-421. doi:10.1016/j.fss.2007.08.016Timón, I., Soto, J., Pérez-Sánchez, H., & Cecilia, J. M. (2016). Parallel implementation of fuzzy minimals clustering algorithm. Expert Systems with Applications, 48, 35-41. doi:10.1016/j.eswa.2015.11.011Flores-Sintas, A., M. Cadenas, J., & Martin, F. (2001). Detecting homogeneous groups in clustering using the Euclidean distance. Fuzzy Sets and Systems, 120(2), 213-225. doi:10.1016/s0165-0114(99)00110-4Wang, H., Potluri, S., Luo, M., Singh, A. K., Sur, S., & Panda, D. K. (2011). MVAPICH2-GPU: optimized GPU to GPU communication for InfiniBand clusters. Computer Science - Research and Development, 26(3-4), 257-266. doi:10.1007/s00450-011-0171-3Kaltofen, E., & Villard, G. (2005). On the complexity of computing determinants. computational complexity, 13(3-4), 91-130. doi:10.1007/s00037-004-0185-3Johnson, S. C. (1967). Hierarchical clustering schemes. Psychometrika, 32(3), 241-254. doi:10.1007/bf02289588Saxena, A., Prasad, M., Gupta, A., Bharill, N., Patel, O. P., Tiwari, A., … Lin, C.-T. (2017). A review of clustering techniques and developments. Neurocomputing, 267, 664-681. doi:10.1016/j.neucom.2017.06.053Woodley, A., Tang, L.-X., Geva, S., Nayak, R., & Chappell, T. (2019). Parallel K-Tree: A multicore, multinode solution to extreme clustering. Future Generation Computer Systems, 99, 333-345. doi:10.1016/j.future.2018.09.038Kwedlo, W., & Czochanski, P. J. (2019). A Hybrid MPI/OpenMP Parallelization of $K$ -Means Algorithms Accelerated Using the Triangle Inequality. IEEE Access, 7, 42280-42297. doi:10.1109/access.2019.2907885Li, Y., Zhao, K., Chu, X., & Liu, J. (2013). Speeding up k-Means algorithm by GPUs. Journal of Computer and System Sciences, 79(2), 216-229. doi:10.1016/j.jcss.2012.05.004Saveetha, V., & Sophia, S. (2018). Optimal Tabu K-Means Clustering Using Massively Parallel Architecture. Journal of Circuits, Systems and Computers, 27(13), 1850199. doi:10.1142/s0218126618501992Djenouri, Y., Djenouri, D., Belhadi, A., & Cano, A. (2019). Exploiting GPU and cluster parallelism in single scan frequent itemset mining. Information Sciences, 496, 363-377. doi:10.1016/j.ins.2018.07.020Krawczyk, B. (2016). GPU-Accelerated Extreme Learning Machines for Imbalanced Data Streams with Concept Drift. Procedia Computer Science, 80, 1692-1701. doi:10.1016/j.procs.2016.05.509Fang, Y., Chen, Q., & Xiong, N. (2019). A multi-factor monitoring fault tolerance model based on a GPU cluster for big data processing. Information Sciences, 496, 300-316. doi:10.1016/j.ins.2018.04.053Tanweer, S., & Rao, N. (2019). Novel Algorithm of CPU-GPU hybrid system for health care data classification. Journal of Drug Delivery and Therapeutics, 9(1-s), 355-357. doi:10.22270/jddt.v9i1-s.244

RESEARCH ISSUES CONCERNING ALGORITHMS USED FOR OPTIMIZING THE DATA MINING PROCESS

In this paper, we depict some of the most widely used data mining algorithms that have an overwhelming utility and influence in the research community. A data mining algorithm can be regarded as a tool that creates a data mining model. After analyzing a set of data, an algorithm searches for specific trends and patterns, then defines the parameters of the mining model based on the results of this analysis. The above defined parameters play a significant role in identifying and extracting actionable patterns and detailed statistics. The most important algorithms within this research refer to topics like clustering, classification, association analysis, statistical learning, link mining. In the following, after a brief description of each algorithm, we analyze its application potential and research issues concerning the optimization of the data mining process. After the presentation of the data mining algorithms, we will depict the most important data mining algorithms included in Microsoft and Oracle software products, useful suggestions and criteria in choosing the most recommended algorithm for solving a mentioned task, advantages offered by these software products.data mining optimization, data mining algorithms, software solutions