Results 151 to 160 of about 4,692,865 (188)

Clustering mixed type data

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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Discovering Functional Dependencies from Mixed-Type Data

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020
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Mandros, Panagiotis, Kaltenpoth, David, Boley, Mario, Vreeken, Jilles   +3 more
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Diagnostic Test for Realized Missingness in Mixed-type Data

Sankhya B, 2023
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Ruizhe Chen, Yu-Che Chung, Sanjib Basu, Qian Shi   +3 more
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Some Cubature Formulae Using Mixed Type Data

2001
We 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, 2014
Due 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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Clustering Mixed-Type Data

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, Zacharenia Kyrana, Nikolaos Papafilippou, Lefkothea Karapetsi, Christos Dordas, Angelos Markos   +5 more
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Visualized mixed-type data analysis via dimensionality reduction

Intelligent Data Analysis, 2018
Visualization 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, 2011
The 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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Rank-based process control for mixed-type data

IIE Transactions, 2016
ABSTRACTConventional statistical process control tools target either continuous or categorical data but seldom both at the same time. However, mixed-type data consisting of both continuous and categorical observations are becoming more common in modern manufacturing processes and service management.
Ding, Dong, Tsung, Fu-gee, Li, Jian
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Clustering Mixed-Type Data with Correlation-Preserving Embedding

2021
Mixed-type data that contains both categorical and numerical features is prevalent in many real-world applications. Clustering mixed-type data is challenging, especially because of the complex relationship between categorical and numerical features. Unfortunately, widely adopted encoding methods and existing representation learning algorithms fail to ...
Luan Tran, Liyue Fan, Cyrus Shahabi
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