Search Results - (( java implication based algorithm ) OR ( data generation means algorithm ))

MaxD K-Means: A clustering algorithm for auto-generation of centroids and distance of data points in clusters by Wan Maseri, Wan Mohd, Beg, Abul Hashem, Herawan, Tutut, Fazley Rabbi, Khandakar

“…In this paper, we propose a clustering technique called MaxD K-Means clustering algorithm. MaxD K-Means algorithm auto generates initial k (the desired number of cluster) without asking for input from the user. …”
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Improved Parameterless K-Means: Auto-Generation Centroids and Distance Data Point Clusters by Wan Maseri, Wan Mohd, Beg, Abul Hashem, Herawan, Tutut, Noraziah, Ahmad

“…K-means is an unsupervised learning and partitioning clustering algorithm. …”
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Improving Classification of Remotely Sensed Data Using Best Band Selection Index and Cluster Labelling Algorithms by Teoh, Chin Chuang

“…In cluster generating process, the developed BBSI algorithm was used to select the best band combination for generating cluster by using Iterative self- Organizing Data Analysis (ISODATA) technique. …”
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Optimized clustering with modified K-means algorithm by Alibuhtto, Mohamed Cassim

“…Generally, the proposed modified k-means algorithm is able to determine the optimum number of clusters for huge data.…”
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A guided hybrid k-means and genetic algorithm models for children handwriting legibility performance assessment / Norzehan Sakamat by Sakamat, Norzehan

“…While hybrid K-MeansCGA combination of fix population size=100 and various size of generation performs better than general KMeans algorithm and hybrid K-MeansCGA combination of fix generation size=100 and various size of populations. …”
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Max-D clustering K-means algorithm for Autogeneration of Centroids and Distance of Data Points Cluster by Wan Maseri, Wan Mohd, Beg, Abul Hashem, Tutut, Herawan, K., F.Rabbi

“…In this paper has been proposed a clustering technique called MaxD K-Means clustering algorithm. MaxD K-Means algorithm auto generates initial k (the desired number of cluster) without asking for input from the user. …”
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Autonomous and deterministic supervised fuzzy clustering by Lim, K.M., Loo, C.K., Lim, W.S.

“…The model is tested on medical diagnosis benchmark data and Westland vibration data. The results obtained show that the model that uses the global k-means clustering algorithm 1 has higher accuracy when compared to a model that uses the k-means clustering algorithm. …”
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Combined generative adversarial network and fuzzy C-means clustering for multi-class voice disorder detection with an imbalanced dataset by Chui, K.T., Lytras, M.D., Vasant, P.

“…In this paper, a conditional generative adversarial network (CGAN) and improved fuzzy c-means clustering (IFCM) algorithm called CGAN-IFCM is proposed for the multi-class voice disorder detection of three common types of voice disorders. …”
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Cluster Analysis of Data Points using Partitioning and Probabilistic Model-based Algorithms by Raheem, Ajiboye Adeleke, Hauwau, Isah-Kebbe, O., Oladele Tinuke

“…Experiments were conducted using k-means, k-medoids and EM-algorithm. The study implements each algorithm using RapidMiner Software and the results generated was validated for correctness in accordance to the concept of external criteria method. …”
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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. …”

Big Data Mining Using K-Means and DBSCAN Clustering Techniques by Fawzia Omer, A., Mohammed, H.A., Awadallah, M.A., Khan, Z., Abrar, S.U., Shah, M.D.

“…The World Wide Web industry generates big and complex data such as web server log files. …”
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VLSI floor planning optimization using genetic algorithm and cross entropy method / Angeline Teoh Szu Fern by Angeline Teoh, Szu Fern

“…This method consists of 2 phases which are the random data generation and then update of the probabilities based on the performance of the data generated. …”
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The new efficient and accurate attribute-oriented clustering algorithms for categorical data by Qin, Hongwu

“…Four real-life data sets obtained from University of California Irvine (UCI) machine learning repository and ten synthetically generated data sets are used to evaluate MGR and IG-ANMI algorithms, and other four algorithms are used to compare with these two algorithms. …”
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Fast and Accuracy Control Chart Pattern Recognition using a New cluster-k-Nearest Neighbor by Brahim Belhaouari, samir

“…In general the algorithm of K-means cluster is not stable, in term of accuracy, for that reason we develop another algorithm for clustering our space which gives a higher accuracy than K-means cluster, less subclass number, stability and bounded time of classi¯cation with respect to the variable data size. …”
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Fast and Accuracy Control Chart Pattern Recognition using a New cluster-k-Nearest Neighbor by Brahim Belhaouari, samir

“…In general the algorithm of K-means cluster is not stable, in term of accuracy, for that reason we develop another algorithm for clustering our space which gives a higher accuracy than K-means cluster, less subclass number, stability and bounded time of classi¯cation with respect to the variable data size. …”
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Accuracy enhancement for zone mapping of a solar radiation forecasting based multi-objective model for better management of the generation of renewable energy by Ehteram M., Ahmed A.N., Fai C.M., Afan H.A., El-Shafie A.

A Hybrid Rough Sets K-Means Vector Quantization Model For Neural Networks Based Arabic Speech Recognition by Babiker, Elsadig Ahmed Mohamed

“…Classification rules were generated from training feature vectors set, and a modified form of the standard voter classification algorithm, that use the rough sets generated rules, was applied. …”
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MINING CUSTOMER DATA FOR DECISION MAKING USING NEW HYBRID CLASSIFICATION ALGORITHM by Aurangzeb, khan, Baharum, Baharudin, Khairullah, khan

“…The experimental result shows that the proposed hybrid k-mean plus MFP algorithm can generate more useful pattern from large stock data.…”
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MINING CUSTOMER DATA FOR DECISION MAKING USING NEW HYBRID CLASSIFICATION ALGORITHM by Aurangzeb, khan, Baharum, Baharudin, Khairullah, Khan

“…The experimental result shows that the proposed hybrid k-mean plus MFP algorithm can generate more useful pattern from large stock data.…”
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Gas Identi cation by Using a Cluster-k-Nearest-Neighbor by Brahim Belhaouari, samir

“…We find 98.7% of accuracy in the classification of 6 different types of Gas by using K-means cluster algorithm and we find almost the same by using the new clustering algorithm.…”
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