Enumeration of splitting types that can be used as splitting creation of cluster in XMeans algorithm. More...
Static Public Attributes  
int  BAYESIAN_INFORMATION_CRITERION = 0 
Bayesian information criterion (BIC) to approximate the correct number of clusters. More...  
int  MINIMUM_NOISELESS_DESCRIPTION_LENGTH = 1 
Minimum noiseless description length (MNDL) to approximate the correct number of clusters. More...  
Enumeration of splitting types that can be used as splitting creation of cluster in XMeans algorithm.

static 
Bayesian information criterion (BIC) to approximate the correct number of clusters.
Kass's formula is used to calculate BIC:
The number of free parameters is simply the sum of class probabilities, centroid coordinates, and one variance estimate:
The loglikelihood of the data:
The maximum likelihood estimate (MLE) for the variance:

static 
Minimum noiseless description length (MNDL) to approximate the correct number of clusters.
Beheshti's formula is used to calculate upper bound:
where and represent the parameters for validation probability and confidence probability.
To improve clustering results some contradiction is introduced: