Search Results - parallel using ((ranking algorithm) OR (mining algorithm))*

Parallel execution of distributed SVM using MPI (CoDLib) by Salleh N.S.M., Suliman A., Ahmad A.R.

“…Instead of using a single machine for parallel computing, multiple machines in a cluster are used. …”

A GPU accelerated parallel genetic algorithm for the traveling salesman problem by Binjubier, Mohammed, Mohd Arfian, Ismail, Tusher, Ekramul Haque, Aljanabi, Mohammad

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Effective software fault localization based on complex network theory / Abubakar Zakari by Abubakar , Zakari

“…In the case where a developer has checked 70% of the program statements and cannot fully localize all the multiple faults in a single diagnosis rank list, rather than resorting to using OBA bugging approach, a newly proposed community-based fault isolation approach that makes use of a divisive network community clustering algorithm is applied to aid in isolating faults into different fault-focused communities (clusters), each targeting a single fault. …”
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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

“…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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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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A novel association rule mining approach using TID intermediate itemset by Aqra, Iyad, Herawan, Tutut, Ghani, Norjihan Abdul, Akhunzada, Adnan, Ali, Akhtar, Bin Razali, Ramdan, Ilahi, Manzoor, Raymond Choo, Kim-Kwang

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A novel association rule mining approach using TID intermediate itemset by Aqra, Iyad, Herawan, Tutut, Norjihan, Abdul Ghani, Akhunzada, Adnan, Ali, Akhtar, Ramdan, Razali, Ilahi, Manzoor, Choo, Kim-Kwang Raymond

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Measuring GPU-accelerated parallel SVM performance using large datasets for multi-class machine learning problem by Sulaiman M.A.H.B., Suliman A., Ahmad A.R.

Twofold Integer Programming Model for Improving Rough Set Classification Accuracy in Data Mining. by Saeed, Walid

“…This research has proven that the TIP method has shown the ability to cater for different kinds of datasets and obtained a good rough classification model with promising results as compared with other commonly used classifiers. This research opens a wide range of future work to be considered, which includes applying the proposed method in other areas such as web mining, text mining or multimedia mining; and extending the proposed approach to work in parallel computing in data mining.…”
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Random sampling method of large-scale graph data classification by Rashed Mustafa, Mohammad Sultan Mahmud, Mahir Shadid

“…Since the data blocks in this model are much smaller than the entire data set, it is more efficient to analyze them on a standalone small machine, and multiple data blocks can be analyzed on multiple nodes of the cluster in parallel. Finally, we classified the graphs of data blocks using the SVM algorithm. …”
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Prognosis of early cervical carcinoma using gene expression profiling by Zarzar, Mouayad, Razak, Eliza, Htike@Muhammad Yusof, Zaw Zaw, Yusof, Faridah

“…Data mining and machine learning have found considerable application thru the use of microarray expression profiling inspection. …”
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Job Matching Mobile Application using Fuzzy Analytic Hierarchy Process (FAHP) / Mohammad Ashraf Jefrizin by Jefrizin, Mohammad Ashraf

“…Findings of this project is portrayed as conceptual framework that consists the structure of job matching mobile application using FAHP algorithm. Evaluation is conducted using accuracy test where the result of this application is compared to the manual method using a survey. …”
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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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