Search Results - (( knowledge utilization clustering algorithm ) OR ( java simulation optimization algorithm ))

Document clustering for knowledge discovery using nature-inspired algorithm by Mohammed, Athraa Jasim, Yusof, Yuhanis, Husni, Husniza

“…As the internet is overload with information, various knowledge based systems are now equipped with data analytics features that facilitate knowledge discovery.This includes the utilization of optimization algorithms that mimics the behavior of insects or animals.This paper presents an experiment on document clustering utilizing the Gravitation Firefly algorithm (GFA).The advantage of GFA is that clustering can be performed without a pre-defined value of k clusters.GFA determines the center of clusters by identifying documents with high force.Upon identification of the centers, clusters are created based on cosine similarity measurement.Experimental results demonstrated that GFA utilizing a random positioning of documents outperforms existing clustering algorithm such as Particles Swarm Optimization (PSO) and K-means.…”
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Determining number of clusters using firefly algorithm with cluster merging for text clustering by Mohammed, Athraa Jasim, Yusof, Yuhanis, Husni, Husniza

“…Such a scenario requires a dynamic text clustering method that operates without initial knowledge on a data collection.In this paper, a dynamic text clustering that utilizes Firefly algorithm is introduced.The proposed, aFAmerge, clustering algorithm automatically groups text documents into the appropriate number of clusters based on the behavior of firefly and cluster merging process. …”
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Efficient genetic partitioning-around-medoid algorithm for clustering by Garib, Sarmad Makki Mohammed

“…These algorithms mostly built upon the partitioning k-means clustering algorithm. …”
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Comparison of clustering algorithms on air quality substances in Peninsular Malaysia / Sitti Sufiah Atirah Rosly, Balkiah Moktar and Muhamad Hasbullah Mohd Razali by Rosly, Sitti Sufiah Atirah, Moktar, Balkiah, Mohd Razali, Muhamad Hasbullah

“…Monthly data from 37 monitoring stations in Peninsular Malaysia from the year 2013 to 2015 were used in this study. K-Means (KM) clustering algorithm, Expectation Maximization (EM) clustering algorithm and Density Based (DB) clustering algorithm have been chosen as the techniques to analyze the cluster analysis by utilizing the Waikato Environment for Knowledge Analysis (WEKA) tools. …”
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Big data clustering using grid computing and ant-based algorithm by Ku-Mahamud, Ku Ruhana

“…This paper presents a framework for big data clustering which utilizes grid technology and ant-based algorithm.…”
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Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection by Nwogbaga, Nweso Emmanuel, Latip, Rohaya, Affendey, Lilly Suriani, Abdul Rahiman, Amir Rizaan

“…The simulation is implemented with iFogSim and java programming language. …”
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Evaluation on rapid profiling with clustering algorithms for plantation stocks on Bursa Malaysia by Keng, Hoong Ng, Kok, Chin Khor

“…In this study, we utilized Expectation Maximization (EM), K-Means (KM), and Hierarchical Clustering (HC) algorithms to cluster the 38 plantation stocks listed on Bursa Malaysia using 14 financial ratios derived from the fundamental analysis.The clustering allows investors to profile each resulted cluster statistically and assists them in selecting stocks for their stock portfolios rapidly.The performance of each cluster was then assessed using 1-year stock price movement.The result showed that a cluster resulted from EM had a better profile and obtained a higher average capital gain as compared with the other clusters.…”
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Cluster identification and separation in the growing self-organizing map: Application in protein sequence classification by Ahmad, N.

“…Growing self-organizing map (GSOM) has been introduced as an improvement to the self-organizing map (SOM) algorithm in clustering and knowledge discovery. Unlike the traditional SOM, GSOM has a dynamic structure which allows nodes to grow reflecting the knowledge discovered from the input data as learning progresses. …”
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Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing by Husna, Jamal Abdul Nasir

“…Global pheromone update is performed after the completion of processing the jobs in order to reduce the pheromone value of resources. A simulation environment was developed using Java programming to test the performance of the proposed EACO algorithm against existing grid resource management algorithms such as Antz algorithm, Particle Swarm Optimization algorithm, Space Shared algorithm and Time Shared algorithm, in terms of processing time and resource utilization. …”
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Ant colony optimization algorithm for load balancing in grid computing by Ku-Mahamud, Ku Ruhana, Mohamed Din, Aniza

“…The proposed algorithm is known as the enhance ant colony optimization (EACO). …”
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Towards lowering computational power in IoT systems: Clustering algorithm for high-dimensional data stream using entropy window reduction by Alkawsi G., Al-amri R., Baashar Y., Ghorashi S., Alabdulkreem E., Kiong Tiong S.

“…The findings of the experiments are compared to the outcomes of BOCEDS, CEDAS, and MuDi-Stream algorithms. The outcomes indicate that the EWR algorithm outperformed the baseline clustering algorithms. …”

Biological-based semi-supervised clustering algorithm to improve gene function prediction by Kasim, Shahreen, Deris, Safaai, M. Othman, Razib, Hashim, Rathiah

“…However, commonclustering algorithms do not provide a comprehensive approach that look into the three categories of annotations; biologicalprocess, molecular function, and cellular component, and were not tested with different functional annotation database formats.Furthermore, the traditional clustering algorithms use random initialization which causes inconsistent cluster generation and areunable to determine the number of clusters involved. …”
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OPTIMIZED MIN-MIN TASK SCHEDULING ALGORITHM FOR SCIENTIFIC WORKFLOWS IN A CLOUD ENVIRONMENT by Murad S.S., Badeel R., Alsandi N.S.A., Alshaaya R.F., Ahmed R.A., Muhammed A., Derahman M.

“…To achieve this, we propose a new noble mechanism called Optimized Min-Min (OMin-Min) algorithm, inspired by the Min-Min algorithm. …”

Embedded system for indoor guidance parking with Dijkstra’s algorithm and ant colony optimization by Mohammad Ata, Karimeh Ibrahim

“…This study proposes a car parking management system which applies Dijkstra’s algorithm, Ant Colony Optimization (ACO) and Binary Search Tree (BST) in structuring a guidance system for indoor parking. …”
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Features selection for intrusion detection system using hybridize PSO-SVM by Tabaan, Alaa Abdulrahman

“…Hybridize Particle Swarm Optimization (PSO) as a searching algorithm and support vector machine (SVM) as a classifier had been implemented to cope with this problem. …”
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Resource management in grid computing using ant colony optimization by Ku-Mahamud, Ku Ruhana, Mohamed Din, Aniza

“…Resources with high pheromone value are selected to process the submitted jobs.Global pheromone update is performed after completion processing the jobs in order to reduce the pheromone value of resources.A simulation environment was developed using Java programming to test the performance of the proposed EACO algorithm against other ant based algorithm, in terms of resource utilization.Experimental results show that EACO produced better grid resource management solution.…”
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Implementation of locust inspired scheduling algorithm with huge number of servers for energy efficiency in a cloud datacenter by Azhar, Nur Huwaina

“…Cloudsim is used as Discrete Event Simulation tool and Java as coding language to evaluate LACE algorithm. …”
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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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Implementation of Hybrid Indexing, Clustering and Classification Methods to Enhance Rural Development Programme in South Sulawesi by Muhammad, Faisal

“…The Fuzzy Tsukamoto and Smallest of Maximum methods were then used to classify villages into less development, which involved CSLI-Clusters as indicators. Using the cosine similarity algorithm for knowledge recommendation is village identified, utilizing community feedback as the foundation. …”
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Chaotic mutation immune evolutionary programming for photovoltaic planning in power system / Sharifah Azma Syed Mustaffa by Syed Mustaffa, Sharifah Azma

“…The results obtained from this study could be utilized by utility companies for planning the installation of DGPV into the grid system for minimization of transmission loss and FVSI value in their systems. …”
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