Search Results - (( knowledge utilization clustering algorithm ) OR ( java adaptation 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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Parallel distributed genetic algorithm development based on microcontrollers framework by Krishnan P.S., Kiong T.S., Koh J.

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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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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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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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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An assessment of sedimentation in Terengganu River, Malaysia using satellite imagery by Aziz A., Essam Y., Ahmed A.N., Huang Y.F., El-Shafie A.

Research on the construction of an efficient and lightweight online detection method for tiny surface defects through model compression and knowledge distillation by Chen, Qipeng, Xiong, Qiaoqiao, Huang, Haisong, Tang, Saihong, Liu, Zhenghong

“…In response to the current issues of poor real-time performance, high computational costs, and excessive memory usage of object detection algorithms based on deep convolutional neural networks in embedded devices, a method for improving deep convolutional neural networks based on model compression and knowledge distillation is proposed. …”
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A detailed description on unsupervised heterogeneous anomaly based intrusion detection framework by Udzir, Nur Izura, Hajamydeen, Asif Iqbal

“…More effort has been taken in utilizing the data mining and machine learning algorithms to construct anomaly based intrusion detection systems, but the dependency on the learned models that were built based on earlier network behaviour still exists, which restricts those methods in detecting new or unknown intrusions. …”
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Real-time anomaly detection using clustering in big data technologies / Riyaz Ahamed Ariyaluran Habeeb by Riyaz Ahamed , Ariyaluran Habeeb

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Predicting the popularity of tweets using the theory of point processes. by Tan, Wai Hong

“…The knowledge is combined using a novel empirical Bayes type approach, where the prior distribution for the model parameter is constructed based on the external knowledge, and the likelihood is calculated based on the internal knowledge. …”
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Anfis Modelling On Diabetic Ketoacidosis For Unrestricted Food Intake Conditions by Saraswati, Galuh Wilujeng

“…The project has also implemented the optimization process onto the proposed ANFIS model through the hybrid of Genetic Algorithm on the fuzzy membership function of the ANFIS model. …”
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Content-based indexing of low resolution documents by Md Nor, Danial

“…We present hierarchy indexing techniques, whose foundation are tree and clustering. K-means clustering are used for visual features like colour since their spatial distribution give a good image’s global information. …”
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