B3

This function determines the extrinsic clustering quality b-cubed measure using a set of known labels for a data set, and cluster assignmnets of each data point stored as either vector or cell arrays where each cell/row represets a data point in which an array containing class or cluster assignments is stored. The multi-class variant should not yet be used and does not scale well.

Python and matlab implementations are provided.

Inputs

   L: An NxM matrix containing class labels for each data point. Each 
      row represents the ith data point and each column contains a 0, or 1
      in the jth column indicating membership of label j.
      Alternatively, if hard class labels are available, L can be input 
      as an Nx1 vector where each entry is its class label

   K: Defined identically to L except for this variable stores cluster
      assignments for each data point

Outputs

  Fmeasure: This F-measure using the b-cubed metric
  precision: The b-cubed precision
  recall: The b-cubed recall

  Author: Matthew Wiesner
  Email : wiesner@jhu.edu 
  Institute: Johns Hopkins University Electrical and Computer Engineering

  Refences: 
       DESCRIPTION OF THE UPENN CAMP SYSTEM AS USED FOR COREFERENCE,
       Breck Baldwin, Tom Morton, Amit Bagga, Jason Baldridge, 
       Raman Chandraseker, Alexis Dimitriadis, Kieran Snyder, 
       Magdalena Wolska, Institute for Research in Cognitive Science

       A.A. Aroch-Villarruel Pattern Recognition 6th Mexican Conference,
       MCPR 2014 Proceedings Paper, p.115