Results 41 to 50 of about 15,644,966 (296)
Clustering with niching genetic K-means algorithm
, 2004 GA-based clustering algorithms often employ either simple GA, steady state GA or their variants and fail to consistently and efficiently identify high quality solutions (best known optima) of given clustering problems, which involve large data sets with ...
Liu, X, Tucker, A, Sheng, W
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Color image segmentation using a spatial k-means clustering algorithm [PDF]
, 2006 This paper details the implementation of a new adaptive technique for color-texture segmentation that is a generalization of the standard K-Means algorithm. The standard K-Means algorithm produces accurate segmentation results only when applied to images
Ilea, Dana E., Whelan, Paul F.
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On the Consistency of k -means++ algorithm [PDF]
Fundamenta Informaticae, 2020 We prove in this paper that the expected value of the objective function of the k-means++ algorithm for samples converges to population expected value. As k-means++, for samples, provides with constant factor approximation for k-means objectives, such an approximation can be achieved for the population with increase of the sample size.
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Interpretation and optimization of the k-means algorithm [PDF]
Applications of Mathematics, 2014 The paper gives a new interpretation and a possible optimization of the well known k-means algorithm for searching for a locally optimal partition of the set A={; ; _ ⋲Rn: =1, …, }; ; which consists of k disjoint non empty subsets 1, , .. , 1≤k≤m. For this purpose, a new divided k-means algorithm was constructed as a limit case of the known smoothed k ...
Sabo, Kristian, Scitovski, Rudolf
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K+ Means : An Enhancement Over K-Means Clustering Algorithm
CoRR, 2017 Authors: Co-author's name added Section 3: Step (a) and (b) of K+Means algorithm are merged for simplicity. Section 3.1: K+ Means algorithm complexity rectified.
Srikanta Kolay +2 more
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Evaluation of modified adaptive k-means segmentation algorithm
Computational Visual Media, 2019 Segmentation is the act of partitioning an image into different regions by creating boundaries between regions. k-means image segmentation is the simplest prevalent approach.
Taye Girma Debelee +3 more
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Jurnal Saintekom, 2023 K-Means algorithm as a method of grouping a set of data. The purpose of this study is to find out the use of the K-Means algorithm for outgoing mail data. The method used in this study focuses on the K-Means method. The grouping data used is 284 outgoing
Lili Rusdiana, Veny Cahya Hardita
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, 2022 S k-means program was developed by a team led by Quang-Van DOAN at the Center for Computational Sciences (CCS), the University of Tsukuba. S k-means can be used by any person or entity for any purpose without any fee or charge.
Van Doan
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Turbid of Water By Using Fuzzy C- Means and Hard K- Means
مجلة بغداد للعلوم, 2020 In this research two algorithms are applied, the first is Fuzzy C Means (FCM) algorithm and the second is hard K means (HKM) algorithm to know which of them is better than the others these two algorithms are applied on a set of data collected from the ...
Rand Muhaned Fawzi, Iden Hassan Alkanani
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