MixAll: Clustering Heterogenous data with Missing Values
نویسنده
چکیده
The Clustering project is a part of the STK++ library (Iovleff 2012) that can be accessed from R (R Development Core Team 2013) using the MixAll package. It is possible to cluster Gaussian, gamma, categorical, Poisson, kernel mixture models or a combination of these models in case of heterogeneous data. Moreover, if there is missing values in the original data set, these missing values will be imputed during the estimation process. These imputations can be biased estimators or Monte-Carlo estimators of the Maximum A Posteriori (MAP) values depending of the algorithm used.
منابع مشابه
MixAll: Clustering Mixed data with Missing Values
The Clustering project is a part of the STK++ library (Iovleff 2012) that can be accessed from R (R Development Core Team 2013) using the MixAll package. It is possible to cluster Gaussian, gamma, categorical, Poisson, kernel mixture models or a combination of these models in case of mixed data. Moreover, if there is missing values in the original data set, these missing values will be imputed ...
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