Results 11 to 20 of about 843,626 (294)
Universal Algorithms for Clustering Problems
ACM Transactions on Algorithms, 2023 This article presentsuniversalalgorithms for clustering problems, including the widely studiedk-median,k-means, andk-center objectives. The input is a metric space containing allpotentialclient locations. The algorithm must selectkcluster centers such that they are a good solution foranysubset of clients that actually realize.
Arun Ganesh +2 more
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Clustering based hybrid approach for facility location problem [PDF]
Management Science Letters, 2017 The main objective of facility location problem is the utilization of the facility by maximum number of possible customers so that the profit is maximized.
Ashish Sharma +2 more
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Improved Ramp-Based Twin Support Vector Clustering [PDF]
Jisuanji kexue yu tansuo, 2023 Twin support vector clustering based on Hinge loss and twin support vector clustering based on Ramp loss are two new twin support vector clustering algorithms, which provide a new research idea for solving the clustering problem, and gradually become a ...
CHEN Sugen, LIU Yufei
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Problems of Clustering of Radiogalaxies [PDF]
Proceedings of the International Astronomical Union, 2012 AbstractWe present the preliminary analysis of clustering of a sample of 1157 radio-identified galaxies from Machalski & Condon (1999). We found that for separations 2–15 h−1 Mpc their redshift space autocorrelation function ξ(s) can be approximated by the power law with the correlation length ~3.75h−1 Mpc and slope γ ~ 1.8.
Godłowski, Włodzimierz +2 more
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A Two-Stage Evolutionary Fuzzy Clustering Framework for Noisy Image Segmentation
IEEE Access, 2020 This article presents a two-stage evolutionary fuzzy clustering framework for noisy image segmentation. It is a bi-stage system comprising a multi-objective optimization stage and a fuzzy clustering segmentation stage. In the multi-objective optimization
Licheng Jiao +4 more
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Approximate Clustering via Metric Partitioning [PDF]
, 2016 In this paper we consider two metric covering/clustering problems - \textit{Minimum Cost Covering Problem} (MCC) and $k$-clustering. In the MCC problem, we are given two point sets $X$ (clients) and $Y$ (servers), and a metric on $X \cup Y$.
Bandyapadhyay, Sayan +1 more
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Robust hierarchical k-center clustering [PDF]
, 2015 One of the most popular and widely used methods for data clustering is hierarchical clustering. This clustering technique has proved useful to reveal interesting structure in the data in several applications ranging from computational biology to computer
Lattanzi, Silvio +3 more
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Greedy Strategy Works for k-Center Clustering with Outliers and Coreset Construction [PDF]
, 2019 We study the problem of k-center clustering with outliers in arbitrary metrics and Euclidean space. Though a number of methods have been developed in the past decades, it is still quite challenging to design quality guaranteed algorithm with low ...
Ding, Hu, Wang, Zixiu, Yu, Haikuo
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A hybrid distance measure for clustering expressed sequence tags originating from the same gene family. [PDF]
PLoS ONE, 2012 BACKGROUND: Clustering is a key step in the processing of Expressed Sequence Tags (ESTs). The primary goal of clustering is to put ESTs from the same transcript of a single gene into a unique cluster.
Keng-Hoong Ng +2 more
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Applied Sciences, 2022 Software maintenance is an important step in the software lifecycle. Software module clustering is a HHMO_CF_GDA optimization problem involving several targets that require minimization of module coupling and maximization of software cohesion.
Haya Alshareef, Mashael Maashi
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