Results 21 to 30 of about 798,948 (275)
Wind and Photovoltaic Generation Scene Division Based on Improved K-means Clustering
发电技术, 2020 In view of the uncertainty of power generation in renewable energy, especially wind power and photovoltaic power generation, the improved K-means clustering method was used to segment the state of power generation.
Xuewei SONG, Yuyao LIU
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Fast density estimation for density-based clustering methods
Neurocomputing, 2023 Density-based clustering algorithms are widely used for discovering clusters in pattern recognition and machine learning since they can deal with non-hyperspherical clusters and are robustness to handle outliers. However, the runtime of density-based algorithms are heavily dominated by finding fixed-radius near neighbors and calculating the density ...
Cheng, Difei +3 more
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Energies, 2022 As an important part of the ubiquitous power Internet of Things, the distribution Internet of Things can further improve the automation and informatization level of the distribution network.
Nan Shao, Yu Chen
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Extended Fast Search Clustering Algorithm : Widely Density Clusters, No Density Peaks
Computer Science & Information Technology ( CS & IT ), 2015 CFSFDP (clustering by fast search and find of density peaks) is recently developed density-based clustering algorithm. Compared to DBSCAN, it needs less parameters and is computationally cheap for its non-iteration. Alex. at al have demonstrated its power by many applications.
Zhang, Wenkai, Li, Jing
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Density-Based Clustering of Social Networks
Journal of the Royal Statistical Society Series A: Statistics in Society, 2022 Abstract The idea of the modal formulation of density-based clustering is to associate groups with the regions around the modes of the probability density function underlying the data. The correspondence between clusters and dense regions in the sample space is here exploited to discuss an extension of this approach to the analysis of ...
Menardi Giovanna, De stefano, Domenico
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Dynamic feature selection for clustering high dimensional data streams [PDF]
, 2019 open access articleChange in a data stream can occur at the concept level and at the feature level. Change at the feature level can occur if new, additional features appear in the stream or if the importance and relevance of a feature changes as the ...
Fahy, Conor, Yang, Shengxiang
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The density-based clustering method for privacy-preserving data mining
Mathematical Biosciences and Engineering, 2019 Privacy-preserving data mining has become an interesting and emerging issue in recent years since it can, not only hide the sensitive information but still mine the meaningful knowledge at the same time.
Jimmy Ming-Tai Wu +5 more
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On clustering procedures and nonparametric mixture estimation [PDF]
, 2015 This paper deals with nonparametric estimation of conditional den-sities in mixture models in the case when additional covariates are available. The proposed approach consists of performing a prelim-inary clustering algorithm on the additional covariates
Auray, Stéphane +2 more
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Density Profiles of ΛCDM Clusters [PDF]
The Astrophysical Journal, 2004 We analyze the mass accretion histories (MAHs) and density profiles of cluster- size halos with virial masses of 0.6-2.5x10^14/h Msun in a flat LCDM cosmology. In agreement with previous studies,we find that the concentration of the density distribution is tightly correlated with the halo's MAH and its formation redshift.During the period of fast mass ...
Tasitsiomi, Argyro +3 more
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Kernel Clustering: Density Biases and Solutions [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019 Kernel methods are popular in clustering due to their generality and discriminating power. However, we show that many kernel clustering criteria have density biases theoretically explaining some practically significant artifacts empirically observed in the past.
Dmitrii Marin +3 more
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