Search Results - (( developing generation clustering algorithm ) OR ( java simulation optimization algorithm ))

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. …”
Get full text
Get full text

Clustering ensemble learning method based on incremental genetic algorithms by Ghaemi, Reza

“…So far, limited genetic-based clustering ensemble algorithms have been developed. …”
Get full text
Get full text

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. …”
Get full text
Get full text

The new efficient and accurate attribute-oriented clustering algorithms for categorical data by Qin, Hongwu

“…Many algorithms for clustering categorical data have been proposed, in which attribute-oriented hierarchical divisive clustering algorithm Min-Min Roughness (MMR) has the highest efficiency among these algorithms with low clustering accuracy, conversely, genetic clustering algorithm Genetic-Average Normalized Mutual Information (G-ANMI) has the highest clustering accuracy among these algorithms with low clustering efficiency. …”
Get full text
Get full text

Cauchy Density-based Algorithm for VANETs Clustering in 3D Road Environments by AL-Obaidi, A.S., Jubair, M.A., Aziz, I.A., Ahmad, M.R., Mostafa, S.A., Mahdin, H., AL-Tickriti, A.T., Hassan, M.H.

“…The development has included a traffic generation model, mobility model for generating driving behavior, and an algorithm for enabling modeling of road curvature based on an adjacency list that defines the road’s points that define a straight-line segment. …”
Get full text
Get full text

MuDi-Stream: A multi density clustering algorithm for evolving data stream by Amini, A., Saboohi, H., Herawan, T., Teh, Y.W.

“…Density-based method has emerged as a worthwhile class for clustering data streams. Recently, a number of density-based algorithms have been developed for clustering data streams. …”
Get full text
Get full text

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. …”
Get full text
Get full text
Get full text

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). …”
Get full text
Get full text
Get full text

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. …”
Get full text
Get full text

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. …”
Get full text
Get full text

Knowledge-based genetic algorithm for multidimensional data clustering by Purnomo, Muhammad Ridwan Andi, Saleh, Chairul, Lagaida, Reny Lituhayu, Hassan, Azmi

Get full text
Get full text
Get full text
Get full text

Social media mining: a genetic based multiobjective clustering approach to topic modelling by Alfred, Rayner, Loo, Yew Jie, Obit, Joe Henry, Lim, Yuto, Haviluddin, Haviluddin, Azman, Azreen

“…Although effective, the performance of the k-means clustering algorithm depends heavily on the initial centroids and the number of clusters, k. …”
Get full text
Get full text

Optimized clustering with modified K-means algorithm by Alibuhtto, Mohamed Cassim

“…In dealing with correlated variables, PCA was embedded in the proposed algorithm. The developed algorithms were tested on uncorrelated and correlated simulated data sets, generated under various conditions. …”
Get full text
Get full text
Get full text
Get full text

A guided hybrid k-means and genetic algorithm models for children handwriting legibility performance assessment / Norzehan Sakamat by Sakamat, Norzehan

“…K-Means algorithm a popular efficient clustering techniques and genetic algorithm a widely used evolutionary algorithm and known for its adaptive nature were combined to determine the level of handwriting legibility for each child. …”
Get full text
Get full text

Efficient genetic partitioning-around-medoid algorithm for clustering by Garib, Sarmad Makki Mohammed

“…These algorithms mostly built upon the partitioning k-means clustering algorithm. …”
Get full text
Get full text

A Clustering Algorithm for Evolving Data Streams Using Temporal Spatial Hyper Cube by Al?amri R., Murugesan R.K., Almutairi M., Munir K., Alkawsi G., Baashar Y.

“…Evaluation based on both the real world and synthetic datasets has proven the superiority of the developed BOCEDS TSHC clustering algorithm over the baseline algorithms with respect to most of the clustering met-rics. � 2022 by the authors. …”

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.…”
Get full text
Get full text

Social media mining: a genetic based multiobjective clustering approach to topic modelling by Rayner Alfred, Loo Yew Jie, Joe Henry Obit, Yuto Lim, Haviluddin Haviluddin, Azreen Azman

“…Although effective, the performance of the k-means clustering algorithm depends heavily on the initial centroids and the number of clusters, k. …”
Get full text
Get full text
Get full text
Get full text

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 density-based spatial clustering of applications with noise (DBSCAN) and the K-means algorithm were used to develop clustering algorithms. …”
Get full text
Get full text

An initial state of design and development of intelligent knowledge discovery system for stock exchange database by Che Mat @ Mohd Shukor, Zamzarina, Khokhar, Rashid Hafeez, Md Sap, Mohd Noor

“…We divide our problem in two modules.In first module we define Fuzzy Rule Base System to determined vague information in stock exchange databases.After normalizing massive amount of data we will apply our proposed approach, Mining Frequent Patterns with Neural Networks.Future prediction (e.g., political condition, corporation factors, macro economy factors, and psychological factors of investors) perform an important rule in Stock Exchange, so in our prediction model we will be able to predict results more precisely.In second module we will generate clustering algorithm. Generally our clustering algorithm consists of two steps including training and running steps.The training step is conducted for generating the neural network knowledge based on clustering.In running step, neural network knowledge based is used for supporting the Module in order to generate learned complete data, transformed data and interesting clusters that will help to generate interesting rules.…”
Get full text
Get full text
Get full text