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Michael R. Chernick
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Cluster analysis is a technique for constructconstructing groups based on on data appearing to be segregated in separate regions. It may have nothing to do with belonging to different groups. Discriminant analysis is a supervised learning method where the data are identified isas belonging to distinct separate groups whether or not they cluster. If the groups cluster well then classification will work well. If you simply create categories on the basis of how they cluster there is no particular meaning to the categories and if nicely spearatedseparated clusters are formed you are guaranteed good discrimination. What would be your motivation for doing this?

Cluster analysis is a technique for construct groups based on on data appearing to be segregated in separate regions. It may have nothing to do with belonging to different groups. Discriminant analysis is a supervised learning method where the data are identified is separate groups whether or not they cluster. If the groups cluster well then classification will work well. If you simply create categories on the basis of how they cluster there is no particular meaning to the categories and if nicely spearated clusters are formed you are guaranteed good discrimination. What would be your motivation for doing this?

Cluster analysis is a technique for constructing groups based on data appearing to be segregated in separate regions. It may have nothing to do with belonging to different groups. Discriminant analysis is a supervised learning method where the data are identified as belonging to distinct separate groups whether or not they cluster. If the groups cluster well then classification will work well. If you simply create categories on the basis of how they cluster there is no particular meaning to the categories and if nicely separated clusters are formed you are guaranteed good discrimination. What would be your motivation for doing this?

Source Link
Michael R. Chernick
  • 43.2k
  • 28
  • 85
  • 159

Cluster analysis is a technique for construct groups based on on data appearing to be segregated in separate regions. It may have nothing to do with belonging to different groups. Discriminant analysis is a supervised learning method where the data are identified is separate groups whether or not they cluster. If the groups cluster well then classification will work well. If you simply create categories on the basis of how they cluster there is no particular meaning to the categories and if nicely spearated clusters are formed you are guaranteed good discrimination. What would be your motivation for doing this?