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Multiple Correspondence K-Means: Simultaneous Versus Sequential Approach for Dimension Reduction and Clustering

机译：多个通信k均值：同时与尺寸减小和聚类的顺序方法

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In this work, a discrete model for clustering and a continuous factorial one for dimension reduction are simultaneously fitted to categorical data, with the aim of identifying the best partition of the objects, described by the best orthogonal linear combinations of the factors, according to the least-squares criterion. This new methodology named multiple correspondence k-means is a useful alternative to the Tandem Analysis in the case of categorical data. Then, this approach has a double objective: data reduction and synthesis, simultaneously in the direction of rows and columns of the data matrix.

机译：在这项工作中，用于聚类的离散模型和用于维度减少的连续因子，同时适用于分类数据，目的是识别由因素的最佳正交线性组合描述的物体的最佳分区。最小二乘标准。这种命名的多个通信k-means的新方法是对分类数据的串联分析的有用替代方案。然后，这种方法具有双重目标：数据减少和合成，同时在数据矩阵的行和列的方向上。

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作者
Mario Fordellone; Maurizio Vichi;
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年度 2017
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原文格式 PDF
正文语种 eng
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入库时间 2022-08-20 22:19:17

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2. Modeling of breakout prediction approach integrating feature dimension reduction with K-means clustering for slab continuous casting [J] . Duan Haiyang, Wang Xudong, Bai Yu, The International Journal of Advanced Manufacturing Technology . 2020,第9a12期

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3. A Model-Based Approach to Simultaneous Clustering and Dimensional Reduction of Ordinal Data [J] . Monia Ranalli, Roberto Rocci Psychometrika . 2017,第4期

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Multiple Correspondence K-Means: Simultaneous Versus Sequential Approach for Dimension Reduction and Clustering

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