Results 11 to 20 of about 1,986,530 (277)
An Overlapping Subspace K-Means Clustering Algorithm [PDF]
Jisuanji gongcheng, 2020 Most of existing clustering algorithms for high-dimensional sparse data do not consider overlapping class clusters and outliers,resulting in unsatisfactory clustering results.Therefore,this paper proposes an overlapping subspace K-Means clustering ...
LIU Yuhang, MA Huifang, LIU Haijiao, YU Li
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Genetic K-means algorithm [PDF]
IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 1999 In this paper, we propose a novel hybrid genetic algorithm (GA) that finds a globally optimal partition of a given data into a specified number of clusters. GA's used earlier in clustering employ either an expensive crossover operator to generate valid child chromosomes from parent chromosomes or a costly fitness function or both.
Krishna, K, Murty, Narasimha M
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Optimized K‐Means Algorithm [PDF]
Mathematical Problems in Engineering, 2014 The localization of the region of interest (ROI), which contains the face, is the first step in any automatic recognition system, which is a special case of the face detection. However, face localization from input image is a challenging task due to possible variations in location, scale, pose, occlusion, illumination, facial expressions, and clutter ...
Samir Brahim Belhaouari +2 more
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Unsupervised Multi-View K-Means Clustering Algorithm
IEEE Access, 2023 Since advanced technologies via social media, internet, virtual communities and networks and internet of things (IoT), there are more multi-view data to be collected.
Miin-Shen Yang, Ishtiaq Hussain
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Zhishi guanli luntan, 2020 [Purpose/significance] From the perspective of industry chain, this paper takes virtual reality technology as an example, constructs VR patent industry chain corpus, and explores the technical theme, research and development hotspot and future ...
Chen Ling, Lin Ping, Duan Yaoqing
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An Incremental K-means algorithm [PDF]
Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2004 Data clustering is an important data exploration technique with many applications in engineering, including parts family formation in group technology and segmentation in image processing. One of the most popular data clustering methods is K-means clustering because of its simplicity and computational efficiency.
Pham, Duc Truong +2 more
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An improved K‐means algorithm for big data
IET Software, 2022 An improved version of K‐means clustering algorithm that can be applied to big data through lower processing loads with acceptable precision rates is presented here.
Fatemeh Moodi, Hamid Saadatfar
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Acta Informatica Pragensia, 2023 This paper presents two novel deterministic initialization procedures for k-means clustering based on a modified crowding distance. The procedures, named ck-means and fck-means, use more crowded points as initial centroids.
Abdesslem Layeb
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A Modified K-means Algorithms - Bi-Level K-Means Algorithm [PDF]
Advances in Intelligent Systems Research, 2014 In this paper, a modified K-means algorithm is proposed to categorize a set of data into smaller clusters. K- means algorithm is a simple and easy clustering method which can efficiently separate a huge number of continuous numerical data with high-dimensions. Moreover, the data in each cluster are similar to one another.
Chia -Yi Chuang +4 more
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K-means Clustering, Unsupervised Classification, K-NN, Euclidean Distance, Genetic Algorithm
Tikrit Journal of Pure Science, 2023 In recent days, the need to provide reliable data transmission over Internet traffics or cellular mobile systems becomes very important. Transmission Control Protocol (TCP) represents the prevailing protocol that provide reliability to data transferring
Maaeda Mohsin Rashid
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