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A class of methods that combine dimension reduction and clustering of continuous, categorical or mixed-type data (Markos, Iodice D'Enza and van de Velden 2019; doi:10.18637/jss.v091.i10). For continuous data, the package contains implementations of factorial K-means (Vichi and Kiers 2001; doi:10.1016/S0167-9473(00)00064-5) and reduced K-means (De Soete and Carroll 1994; doi:10.1007/978-3-642-51175-2_24); both methods that combine principal component analysis with K-means clustering. For categorical data, the package provides MCA K-means (Hwang, Dillon and Takane 2006; doi:10.1007/s11336-004-1173-x), i-FCB (Iodice D'Enza and Palumbo 2013, doi:10.1007/s00180-012-0329-x) and Cluster Correspondence Analysis (van de Velden, Iodice D'Enza and Palumbo 2017; doi:10.1007/s11336-016-9514-0), which combine multiple correspondence analysis with K-means. For mixed-type data, it provides mixed Reduced K-means and mixed Factorial K-means (van de Velden, Iodice D'Enza and Markos 2019; doi:10.1002/wics.1456), which combine PCA for mixed-type data with K-means.
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