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Complex Dimensionality Reduction: Ultrametric Models for Mixed-Type Data
2022The factorial latent structure of variables, if present, can be complex and generally identified by nested latent concepts ordered in a hierarchy, from the most specific to the most general one. This corresponds to a tree structure, where the leaves represent the observed variables and the internal nodes coincide with latent concepts defining the ...
marco mingione +2 more
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On Visualizing Mixed-Type Data
Sociological Methods & Research, 2016Survey data are usually of mixed type (quantitative, multistate categorical, and/or binary variables). Multidimensional scaling (MDS) is one of the most extended methodologies to visualize the profile structure of the data. Since the past 60s, MDS methods have been introduced in the literature, initially in publications in the psychometrics area ...
Aurea Grané, Rosario Romera
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2022
Clustering mixed type data has been receiving increasing attention in the last few years due to the fact that combinations of numeric and categorical data are more common in most business applications. In this thesis we review the related literature (Foss et al., 2016; Foss and Markatou, 2018; Szepannek, 2018; McParland and Gormley, 2016; Marbac et al.,
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Clustering mixed type data has been receiving increasing attention in the last few years due to the fact that combinations of numeric and categorical data are more common in most business applications. In this thesis we review the related literature (Foss et al., 2016; Foss and Markatou, 2018; Szepannek, 2018; McParland and Gormley, 2016; Marbac et al.,
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Discovering Functional Dependencies from Mixed-Type Data
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020No description ...
Mandros, Panagiotis +3 more
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Diagnostic Test for Realized Missingness in Mixed-type Data
Sankhya B, 2023zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ruizhe Chen +3 more
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Some Cubature Formulae Using Mixed Type Data
2001We study some cubature formulae for integrals on I 2 = [-1, 1]2 that use two types of information for the integrand: line integrals over either the boundary of I 2 or the coordinate axes, and evaluations at the points of a uniform grid. The error of these cubature formulae is analyzed, in particular the exact Peano constants are found for some classes ...
Vesselin Gushev, Geno Nikolov
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Hybrid data labeling algorithm for clustering large mixed type data
Journal of Intelligent Information Systems, 2014Due to enormous growth in both volume and variety of data, clustering a very large database is a time-consuming process. To speed up clustering process, sampling has been recognized as a very utilitarian approach to reduce the dataset size in which a collection of data points are taken as a sample and then a clustering algorithm is applied to ...
Ravi Sankar Sangam, Hari Om
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A multidimensional and multivariate structure with mixed-type data allows researchers to use various statistical approaches and data clustering techniques. The choice of clustering method used can have an impact on the results obtained In this study, the Partitioning Clustering (k-means) and Hierarchical Cluster Analysis methods were compared. The main
Emmanouil D. Pratsinakis +5 more
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Emmanouil D. Pratsinakis +5 more
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Visualized mixed-type data analysis via dimensionality reduction
Intelligent Data Analysis, 2018Visualization is a useful technique in data analysis, especially, in the initial stage, data exploration. Since high-dimensional data is not visible, dimensionality reduction techniques are usually used to reduce the data to a lower dimension, say two, for visualization.
Hsu, Chung-Chian, Wu, Jhen-Wei
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Genetic algorithm for clustering mixed-type data
Journal of Electronic Imaging, 2011The k-modes algorithm was recently proposed to cluster mixed-type data. However, in solving clustering problems, the k-modes algorithm and its variants usually ask the user to provide the number of clusters in the data sets. Unfortunately, the number of clusters is generally unknown to the user. Therefore, clustering becomes a tedious task of trial-and-
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