Eye-Clustering: An Enhanced Centroids Prediction for K-means Algorithm حسب Nasser, Youssef

"…This work aims to enhance the performance of the K-means algorithm by introducing a novel method for selecting the initial centroids, thereby minimizing randomness and reducing the number of iterations needed to reach optimal results. …"
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A SIMULATED ANNEALING ALGORITHM FOR THE CLUSTERING PROBLEM حسب Selim, S.Z.

"…In this paper we discuss the solution of the clustering problem usually solved by the K-means algorithm. The problem is known to have local minimum solutions which are usually what the K-means algorithm obtains. …"
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NEW ALGORITHMS FOR SOLVING THE FUZZY CLUSTERING PROBLEM حسب Kamel, M.S.

"…The performance of the new algorithms is compared with the fuzzy c-means algorithm by testing them on four published data sets. …"
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A GLOBAL ALGORITHM FOR THE FUZZY CLUSTERING PROBLEM حسب Selim, S.Z.

"…Some preliminary computational experiments are reported and the solution is compared with that generated by the Fuzzy C-means algorithm.…"
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A Hard Clustering Approach To The Part Family Formation Problem حسب AlSultan, K.S.

"…The problem is cast into a hard clustering model, and the K-means algorithm is proposed for solving it. Preliminary computational experience on the algorithm is very encouraging and it shows that real-life problems of large sizes can efficiently be handled by this approach.…"
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A TABU SEARCH APPROACH TO THE CLUSTERING PROBLEM حسب AlSultan, K.S.

"…It has been studied by many researchers and the most well-known algorithm for solving it is the k-means algorithm. In this paper, we develop a new algorithm for solving this problem based on a tabu search technique. …"
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Computational Experience On Four Algorithms For The Hard Clustering Problem حسب AlSultan, K.S.

"…Several algorithms have been developed to solve this problem which include the k-means algorithm, the simulated annealing algorithm, the tabu search algorithm, and the genetic algorithm. …"
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A Survey of Data Clustering Techniques حسب Sobeh, Salma

"…This survey examines seven widely recognized clustering techniques, namely k-means, G-means, DBSCAN, Agglomerative hierarchical clustering, Two-stage density (DBSCAN and k-means) algorithm, Two-levels (DBSCAN and hierarchical) clustering algorithm, and Two-stage MeanShift and K-means clustering algorithm and compares them over a real dataset - The Blockchain dataset, including prominent cryptocurrencies like Binance, Bitcoin, Doge, and Ethereum, under several metrics such as silhouette coefficient, Calinski-Harabasz, Davies-Bouldin Index, time complexity, and entropy.…"
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Blue collar laborers’ travel pattern recognition: Machine learning classifier approach حسب Aya Hasan Alkhereibi (17151070)

"…A bagged Clustering algorithm was employed to identify the number of clusters, then the C-Means algorithm and the Pamk algorithm were implemented to validate the results. …"