ClusterMatchStrategy#

class ClusterMatchStrategy(value)[source]#

Bases: Enum

Enumeration of strategies for matching clusters to distribution models.

This enumeration defines the available methods for assigning clusters (identified by clustering algorithms) to specific distribution models during mixture model initialization.

Variables:

  • LIKELIHOOD (ClusterMatchStrategy) – Uses weighted log-likelihood criteria to match clusters to models. Each model is sequentially assigned to the cluster that maximizes its weighted log-likelihood score.

  • AKAIKE (ClusterMatchStrategy) – Uses Akaike Information Criterion (AIC) to find the optimal assignment between clusters and models. Evaluates all possible permutations and selects the combination that minimizes the total AIC score.

Notes

LIKELIHOOD Strategy

  • Sequential greedy assignment

  • Computationally efficient

  • May find locally optimal but not globally optimal assignments

  • Uses normalized weighted log-likelihood as selection criteria

AKAIKE Strategy

  • Evaluates all possible cluster-model permutations

  • Finds globally optimal assignment (with respect to AIC)

  • Computationally more expensive but provides better results

  • Balances model fit and complexity through AIC penalty

Comparison

  • LIKELIHOOD: Faster, suitable for large numbers of components

  • AKAIKE: More accurate, recommended for smaller numbers of components

  • Choice depends on computational constraints and quality requirements

Future Extensions

Additional strategies that could be added: - BAYESIAN: Using Bayesian Information Criterion (BIC)

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