Code and Results for Chapter 6:

Introduction:
These are results and code for the problems and examples found in Chapter 6 of this famous book.

• Modified ETA Toolbox Functions
  • mixclass.m (computes the cluster posteriori probability and density estimate at each data point)
    
• prob_6_1.m (model-based agglomerative clustering the oronsay data)
  
• prob_6_2.m (model-based clustering the oronsay data)
  
• prob_6_7_skulls.m (model-based agglomerative clustering the skulls data)
  
• prob_6_7_sparrow.m (model-based agglomerative clustering the sparrow data)
  
• prob_6_7_lungB.m (model-based agglomerative clustering the lungB data)
  
• prob_6_9.m (two or four clusters for the iris data)
  
• prob_6_15.m (the iris data with two clusters)
  
• prob_6_17_skulls.m (model-based agglomerative clustering the skulls data)
  
• prob_6_17_sparrow.m (model-based agglomerative clustering the sparrow data)
  
• prob_6_17_lungB.m (model-based agglomerative clustering the lungB data)
  
• gap_uniform.m (the uniform gap statistic modified for agglomerative clustering)
  
• NEC.m (the NEC cluster criterion function)