Metrics for Clustering Comparison in Bioinformatics

@inproceedings{Rossi2016MetricsFC,
  title={Metrics for Clustering Comparison in Bioinformatics},
  author={Giovanni Rossi},
  booktitle={ICPRAM},
  year={2016}
}
Developing from a concern in bioinformatics, this work analyses alternative metrics between partitions. From both theoretical and applicative perspectives, a useful and interesting distance between any two partitions is HD, which counts the number of atoms finer than either one but not both. While faithfully reproducing the traditional Hamming distance between subsets, HD is very sensible and computable through scalar products between Boolean vectors. It properly deals with… 

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