Search Results - parallel data clustering algorithm*

An efficient parallel clustering algorithm on big data using Spark by Mallik, Moksud Alam, Zulkurnain, Nurul Fariza, Nizamuddin, Mohammed Khaja, Sarkar, Rashel, Ahmed, S K Jamil

“…Here we are proposing a new parallel fuzzy clustering algorithm called "An efficient parallel clustering algorithm on big data using spark" which deals with real-time processing. …”
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The Parallel Fuzzy C-Median Clustering Algorithm Using Spark for the Big Data by Mallik, Moksud Alam, Zulkurnain, Nurul Fariza, Siddiqui, Sumrana, Sarkar, Rashel

“…Therefore, we develop a Parallel Fuzzy C-Median Clustering Algorithm Using Spark for Big Data that can handle large datasets while maintaining high accuracy and scalability. …”
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Parallelization of noise reduction algorithm for seismic data on a beowulf cluster by Aziz, I. A., Sandran, T., Haron, N. S., Hasan, M. H, Mehat, M.

“…This paper presents the parallelization of a sequential noise reduction algorithm for seismic data processing into a parallel algorithm. …”
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Parallel power load abnormalities detection using fast density peak clustering with a hybrid canopy-K-means algorithm by Al-Jumaili A.H.A., Muniyandi R.C., Hasan M.K., Singh M.J., Paw J.K.S., Al-Jumaily A.

“…Data vectors are generated based on the time needed, sequential and parallel candidate feature data are obtained, and the data rate is combined. …”

Parallel genetic algorithms for shortest path routing in high- performance computing / Mohd Erman Safawie Che Ibrahim by Che Ibrahim, Mohd Erman Safawie

“…This project focuses on step-up cluster computing and a parallel Genetic Algorithm. …”
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A spark-based parallel fuzzy C median algorithm for web log big data by Mallik, Moksud Alam, Zulkurnain, Nurul Fariza, Nizamuddin, Mohammed Khaja, Sarkar, Rashal, Chalil, Aboosalih Kakkat

“…At the moment, the situation is such that we are starved for knowledge while drowning in data. Due to these factors, the data mining clustering technique is one of the most crucial tools for collecting useful data from the web. …”
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Parallel execution of distributed SVM using MPI (CoDLib) by Salleh N.S.M., Suliman A., Ahmad A.R.

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Development of a genetic-based hierarchical agglomerative clustering technique for parallel clustering of bilingual corpora based on reduced terms by Rayner Alfred, Jason Teo, Chung, Seng Kheau

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The AGEB algorithm for solving the heat equation in two space dimensions and its parallelization on a distributed memory machine by Alias N., Sahimi M.S., Abdullah A.R.

A genetic-based HAC technique for parallel clustering of bilingual Malay-English corpora by Ng Zhen Wei, Chan Chen Jie, Rayner Alfred, Joe Henry Obit

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Dynamic partitioning and data allocation method on heterogeneous architecture / Muhammad Helmi Rosli by Rosli, Muhammad Helmi

“…Processing images are computationally complex due to its data and task intensive nature. This can be solved by parallelizing the process in cluster environment. …”
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The AGEB Algorithm for Solving Heat Equation in Two Space Dimensions and Its Parallelization on a Distributed Memory Machine by Alias, Norma, Sahimi, Mohamed Salleh, Abdullah, Abdul Rahman

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Mathematical simulation for 3-dimensional temperature visualization on open source-based grid computing platform by Alias, Norma, Satam, Noriza, Abd. Ghaffar, Zarith Safiza, Darwis, Roziha, Hamzah, Norhafiza, Islam, Md. Rajibul

“…New Iterative Alternating Group Explicit (NAGE) is a powerful parallel numerical algorithm for multidimensional temperature prediction. …”
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Investigation on the dynamic of computation of semi autonomous evolutionary computation for syntactic optimization of a set of programming codes by Mohammad Sigit Arifianto, Tze, Kenneth Kin Teo, Liau, Chung Fan, Liawas Barukang, Zaturrawiah Ali Omar

“…Genetic Algorithm as one of the Evolutionary Computation method improve the execution of parallel programming codes by optimizing the number of processors and the distribution of data. …”
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Parallel Processing of Forward-backward Time-stepping Method for Time Domain Inverse Scattering by Kismet, Hong Ping, Moriyama, Toshifumi, Yamaguchi, Yuji, Tanaka, Toshiyuki, Takenaka, Takashi

“…A 3-D reconstruction of wooden hollow cylinder from the experimental data is examined by parallel FBTS algorithm. It is shown that the parallel processing reduces the calculation time and FBTS algorithm provides proper reconstructed profiles of target with respect to relative permittivity and conductivity.…”
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Parallel Implementation of Two Level Barotropic Models Applied to the Weather Prediction Problem by Bahri, Susila

“…Then the parallel algorithms are constructed and run using the Beowulf Cluster machine. …”
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K Nearest Neighbor Joins And Mapreduce Process Enforcement For The Cluster Of Data Sets In Bigdata by Md Shah, Wahidah, Othman, Mohd Fairuz Iskandar, Hussian Hassan, Ali Abdul, Talib, Mohammed Saad, Mohammed, Ali Abdul Jabbar

“…K Nearest Neighbor Joins (KNN join) are regarded as highly primitive and expensive operations in the data mining.The efficient use of KNN join has proven good results in finding the objects from two data sets prevailed in the huge databases.This has been achieved with the combination of K-Nearest Neighbor query and join operation to find the distinct objects from different data sets.MapReduce is a newly introduced program with the combination of Map Procedure method and Reduce Method widely used in BigData.MapReduce is enriched with parallel distributed algorithm to find the results on a cluster of data sets in BigData.In this paper,the combination of KNN join and MapReduce methods are utilized on the cluster of data sets in BigData for knowledge discovery.Exploring the pinpoint data from huge data sets stored in Big Data demands the distributed large scale data processing.The present research paper is focusing on generic steps for KNN joins exploration operations on MapReduce.The operations of KNN Join are targeted to perform the data partitioning and data pre-processing and necessary calculations.By utilizing the combination of KNN joins with MapReduce methods on BigData data sets will demonstrate a solution for complex computational analysis. …”
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Quantification and segmentation of breast cancer diagnosis: efficient hardware accelerator approach by Othman, Khairulnizam

“…Segmentation clustering algorithms have setbacks on overlapping clusters, proportion, and multidimensional scaling to map and leverage the data. …”
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Parallel batch self-organizing map on graphics processing unit using CUDA by Daneshpajouh, H., Delisle, P., Boisson, J.-C., Krajecki, M., Zakaria, N.

“…The most computationally expensive parts of its training algorithm (such as steps to compute distance between each data vector and neuron, and determining the Best Matching Unit based on minimum distance) are identified and mapped on GPU to be processed in parallel. …”
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Parallel batch self-organizing map on graphics processing unit using CUDA by Daneshpajouh, H., Delisle, P., Boisson, J.-C., Krajecki, M., Zakaria, N.

“…The most computationally expensive parts of its training algorithm (such as steps to compute distance between each data vector and neuron, and determining the Best Matching Unit based on minimum distance) are identified and mapped on GPU to be processed in parallel. …”
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