Results 11 to 20 of about 795,774 (269)
Frontiers in Marine Science, 2021 We study the daily to interannual variability of the Red River plume in the Gulf of Tonkin from numerical simulations at high resolution over 6 years (2011–2016). Compared with observational data, the model results show good performance.
Tung Nguyen-Duy +13 more
doaj +1 more source
, 2000 As an explorative technique, duster analysis provides a description or a reduction in the dimension of the data. It classifies a set of observations into two or more mutually exclusive unknown groups based on combinations of many variables. Its aim is to construct groups in such a way that the profiles of objects in the same groups are relatively ...
Mucha, Hans-Joachim, Sofyan, Hizir
openaire +3 more sources
Cluster Correspondence Analysis [PDF]
Psychometrika, 2017 A method is proposed that combines dimension reduction and cluster analysis for categorical data by simultaneously assigning individuals to clusters and optimal scaling values to categories in such a way that a single between variance maximization objective is achieved.
van de Velden, M. +2 more
openaire +5 more sources
Core Collection Formation in Guatemalan Wild Avocado Germplasm with Phenotypic and SSR Data
Agronomy, 2023 Guatemala’s wild avocado germplasm holds vital genetic value, but lacking conservation strategies imperils it. Studying its diversity is pivotal for conservation and breeding.
José Alejandro Ruiz-Chután +6 more
doaj +1 more source
Classification of Financial Events and Its Effects on Other Financial Data
Axioms, 2023 This research classifies financial events, i.e., the collapse of the Lehman Brothers (2008) and the flash crash (2010), and their effects on two different stocks corresponding to Citigroup Inc.
Maria C. Mariani +4 more
doaj +1 more source
Clustered regression analysis [PDF]
Proceedings of the 41st IEEE Conference on Decision and Control, 2002., 2003 Cluster structure in (multicollinear) data can be utilized by pattern recognition methods in order to find adequate subspaces for nonlinear regression. When regressing a particular severely nonlinear function, it is demonstrated that this approach is superior to polynomial PLS.
Lindgren, David, Ljung, Lennart
openaire +2 more sources
Discriminative cluster analysis [PDF]
Proceedings of the 23rd international conference on Machine learning - ICML '06, 2006 Clustering is one of the most widely used statistical tools for data analysis. Among all existing clustering techniques, k-means is a very popular method because of its ease of programming and because it accomplishes a good trade-off between achieved performance and computational complexity.
Fernando De la Torre, Takeo Kanade
openaire +1 more source
Analysis of Agglomerative Clustering [PDF]
Algorithmica, 2012 The diameter $k$-clustering problem is the problem of partitioning a finite subset of $\mathbb{R}^d$ into $k$ subsets called clusters such that the maximum diameter of the clusters is minimized. One early clustering algorithm that computes a hierarchy of approximate solutions to this problem (for all values of $k$) is the agglomerative clustering ...
Marcel R. Ackermann +3 more
openaire +6 more sources
Research on coal rock recognition algorithm and “self-learning” model
Meikuang Anquan, 2021 Aiming at the problems of poor effect, poor stability and small application range of coal and rock recognition methods in fully mechanized coal mining face, based on the difference between the basic characteristics of coal and rock, from the visual ...
ZHANG Wei, FU Yuan, LIU Xin
doaj +1 more source
CES Transactions on Electrical Machines and Systems, 2019 Effective storage, processing and analyzing of power device condition monitoring data faces enormous challenges. A framework is proposed that can support both MapReduce and Graph for massive monitoring data analysis at the same time based on Aliyun ...
Hongtao Shen, Peng Tao, Pei Zhao, Hao Ma
doaj +1 more source