[PDF] Top 20 Covariance-based Clustering in Multivariate and Functional Data Analysis
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Covariance-based Clustering in Multivariate and Functional Data Analysis
... of data in each sub-population, K, is high with respect to their dimensionality, P = 2, we used Max-Swap algorithm in combination with the standard sample estimator of covariance, ...the clustering ... See full document
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Happ, Clara Maria (2017): Statistical methods for data with different dimensions: multivariate functional PCA and scalar-on-image regression. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik
... for multivariate data to the functional case, by simply apply- ing them to the coefficient vectors and transforming the result back to the original ...component analysis (Ramsay and Silverman, ... See full document
226
High-Dimensional Linear and Functional Analysis of Multivariate Grapevine Data
... decades, data collection technology has evolved to measure observations densely sampled over time, wavelength, space and other ...of data, it is more natural to think in functional terms even though ... See full document
134
Exploratory data analysis and clustering of multivariate spatial hydrogeological data by means of GEO3DSOM, a variant of Kohonen's Self Organizing Map
... geospatial data and numerous applications of the technique in geospatial data analysis have proven to be successful (Takatsuka, 2001; Skupin and Hagelman, 2003; Koua et ...the clustering or ... See full document
13
The study of the effectiveness of teaching philosophy in the form of a loop and exploring the working memory of blind students
... of data for each of the memory components in each of the groups is ...investigated. Multivariate covariance analysis requires testing the independence of dependent variables (memory ... See full document
10
A clustering algorithm for multivariate data streams with correlated components
... Common clustering algorithms require multiple scans of all the data to achieve conver- gence, and this is prohibitive when large databases, with data arriving in streams, must be ...the ... See full document
20
Bayesian Graphical Models for Multivariate Functional Data
... vector-valued data are well established, functional data graphical mod- els remain ...By functional data, we refer to data that are realizations of random functions varying over ... See full document
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Serial and parallel implementations of model based clustering via parsimonious Gaussian mixture models
... Model-based clustering using a family of Gaussian mixture models, with parsimo- nious factor analysis-like covariance structure, is described and an efficient algorithm for its implementation ... See full document
25
Clustering multivariate functional data with phase variation
... subjects based on the phase variation differentiating joint growth ...methodology based on multivariate FPCA could be used ...k-means-like clustering algorithm to three-dimensional curves with ... See full document
10
Mixture Model Clustering Using Variable Data Segmentation and Model Selection: A Case Study of Genetic Algorithm
... model clustering based on variable data segmentation and model selection will be explained on a data set, known as namely Ruspini data set ...model-based clustering. ... See full document
10
Exponential family mixed membership models for soft?clustering of multivariate data
... model-based clustering methods have successfully tack- led many of the challenges presented by ...of data analysis has evolved, some problems may be beyond the standard mixture model ...soft ... See full document
22
A Parallel Clustering Method Study Based on MapReduce
... unsupervised clustering result is a difficult ...Component Analysis (PCA), Self Organization Map (SOM) network, and so ...is based on techniques of representing a set of observations by a set of ... See full document
8
K-MEANS Clustering with a Covariance Matrix
... K-means clustering technique employs Euclidean distance metric which primarly aims to reduce the within- cluster ...the data of preceding iteration to be used in the succeeding ...the data point to ... See full document
8
Social Competence of Students with Learning Disability: Advantages of Verbal Self-Instructional Package
... are based in perceptually mediated skills, and learning disabled children differ from non-learning disabled peers in both verbal and perceptual domains of cognitive ... See full document
6
Health through martial arts training: Physical fitness and reaction time in adolescent Taekwondo practitioners
... The major limitation of this study was its cross-sec- tional research design. We cannot be sure whether the observed difference (in reaction time) was the result of nature (e.g., genetics) or nurture (e.g., TKD ... See full document
5
Clustering based information retrieval with the aco and the k-means clustering algorithm
... the pre-processing of the documents. Then, the required features for the information retrieval are selected with the use of the ACO algorithm. Then, the features are subjected to the dynamic reduction scheme. Then, the ... See full document
6
Dubbing Modernization: The United States, France, and the Politics of Development in the Ivory Coast, 1946 1968
... Clustering techniques have been developed to divide a large group of observations into smaller groups such that the observations within each group are relatively similar to each other and the observations in ... See full document
83
Multivariate spatial statistical analysis of longitudinal data in perennial crops
... spatial analysis for this multivariate ...spatial analysis for the univariate case in this experiment was low as a function of the low environmental variability in the ...the multivariate case ... See full document
24
An Approach to Visualization and Clustering-based Analysis on Spatiotemporal Data
... the clustering algorithm with a group of input data, to optimize the cost of the data mining process as a ...no data to ...a data request is received from the control layer, the ... See full document
12
Gaussian tree constraints applied to acoustic linguistic functional data
... feature extraction whilst also reducing unwanted noise. Second, if subsequent to the reduction it is found that n ≥ p then techniques which make use of inverse covariances can be implemented straightforwardly. If this is ... See full document
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