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Monitoring a process with mixed-type and high-dimensional data
2010 IEEE International Conference on Industrial Engineering and Engineering Management, 2010Statistical process control (SPC) techniques that originated in manufacturing have also been applied to monitoring various service processes. The quality of a service process can be characterized by one or several variables. Conventional multivariate SPC methods usually assume these process variables follow an underlying distribution, generally ...
Xianghui Ning, Fugee Tsung
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Clustering Mixed-Type Data with Correlation-Preserving Embedding
2021Mixed-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 ...
Cyrus Shahabi, Luan Tran, Liyue Fan
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Unsupervised evolutionary clustering algorithm for mixed type data
IEEE Congress on Evolutionary Computation, 2010In this paper, we propose a novel unsupervised evolutionary clustering algorithm for mixed type data, evolutionary k-prototype algorithm (EKP). As a partitional clustering algorithm, k-prototype (KP) algorithm is a well-known one for mixed type data. However, it is sensitive to initialization and converges to local optimum easily.
Maoguo Gong +4 more
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Sequential dimension reduction and clustering of mixed-type data
International Journal of Data Analysis Techniques and Strategies, 2020Clustering of a set of objects described by a mixture of continuous and categorical variables can be a challenging task. In the context of data reduction, an effective class of methods combine dimension reduction with clustering in the reduced space. In this paper, we review three approaches for sequential dimension reduction and clustering of mixed ...
Angelos Markos +2 more
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Improving visualization of mixed-type data with a dynamic SOM
2011 Seventh International Conference on Natural Computation, 2011Self-Organizing Map (SOM) possesses an effective visualization capability for supporting analysts efficiently extract valuable information from a large amount of high-dimensional data. Growing SOMs were proposed to overcome the constraint of fixed-size map in conventional SOMs.
Wei-Shen Tai, Chung-Chian Hsu
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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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K-Centers Algorithm for Clustering Mixed Type Data
2007The K-modes and K-prototypes algorithms both apply the frequency-based update method for centroids, regarding attribute values with the highest frequency but neglecting other attribute values, which affects the accuracy of clustering results. To solve this problem, the K-centers clustering algorithm is proposed to handle mixed type data.
Weihui Dai, Chun-Bin Tang, Weidong Zhao
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An overview of real‐world data sources for oncology and considerations for research
Ca-A Cancer Journal for Clinicians, 2022Donna R Rivera +2 more
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