Supervised and Unsupervised Model-Based Multi-Partitioning

نویسندگان

  • Alessio Farcomeni
  • Maurizio Vichi
چکیده

Rocci and Vichi (2006) have recently introduced the two-mode multi-partitioning model with the aim to cluster both objects (rows) and variables (columns) of a two-way data matrix. The new methodology allows to partition the set of objects and to obtain a partition of the variables for each class of the partitions of the objects. In this paper a model-based approach in the field of the maximum likelihood clustering is proposed. The model is extended to the supervised classification framework. A specific algorithm is introduced and its performances are discussed by means of a simulation study. Finally, the new methodology is applied to data sets to show its features.

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تاریخ انتشار 2007