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Rank-based process control for mixed-type data
IIE Transactions, 2016ABSTRACTConventional 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
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 ...
Luan Tran, Liyue Fan, Cyrus Shahabi
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External Logistic Biplots for Mixed Types of Data
2020A simultaneous representation of individuals and variables in a data matrix is called a biplot. When variables are binary, nominal, or ordinal, a classical linear biplot representation is not adequate. Recently, biplots for categorical data-based logistic response models have been proposed.
José L. Vicente-Villardón +1 more
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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.
Zhi Zheng +4 more
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Distance function for mixed type data
2007There are several strategies to cope with the simultaneous presence of different measurement scales. A reasonable option would be to compute the dissimilarity matrix for each type of variable: bynary, categorical, ordinal and metric. Then a compromise dissimilarity matrix can be achieved by using a convex combination of all the partial matrices ...
TARSITANO, Agostino, Bonafine I.
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Clustering bivariate mixed-type data via the cluster-weighted model
Computational Statistics, 2015zbMATH Open Web Interface contents unavailable due to conflicting licenses.
PUNZO, ANTONIO, INGRASSIA, Salvatore
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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.
Wei-Dong Zhao +2 more
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k-SubMix: Common Subspace Clustering on Mixed-Type Data
2023Klein, Mauritius +2 more
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