Interactive Clustering and Classification


In this paper, we propose a general framework referred to as interactive clustering and classification (ICC). It is designed to identify sub-groups of samples with different model structures. The framework features an interaction between classification and clustering process, allowing the clustering process to be partially driven by the classification process and is therefore, presumably, more informative. The method is tested rigorously on both synthetic datasets and real world problems. Experimental results demonstrate that ICC provided a good approximation of complex model structure by an aggregation of simple models while circumventing the issue of over-fitting.

  • Abstract
  • 1 Introduction
  • 2 Interactive Clustering and Classification
  • 3. Preliminary Experimental Results
  • 4. Conclusions
  • Reference

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