.. _example_cluster_plot_adjusted_for_chance_measures.py: ========================================================== Adjustment for chance in clustering performance evaluation ========================================================== The following plots demonstrate the impact of the number of clusters and number of samples on various clustering performance evaluation metrics. Non-adjusted measures such as the V-Measure show a dependency between the number of clusters and the number of samples: the mean V-Measure of random labeling increases significantly as the number of clusters is closer to the total number of samples used to compute the measure. Adjusted for chance measure such as ARI display some random variations centered around a mean score of 0.0 for any number of samples and clusters. Only adjusted measures can hence safely be used as a consensus index to evaluate the average stability of clustering algorithms for a given value of k on various overlapping sub-samples of the dataset. .. rst-class:: horizontal * .. image:: images/plot_adjusted_for_chance_measures_001.png :scale: 47 * .. image:: images/plot_adjusted_for_chance_measures_002.png :scale: 47 **Script output**:: Computing adjusted_rand_score for 10 values of n_clusters and n_samples=100 done in 0.150s Computing v_measure_score for 10 values of n_clusters and n_samples=100 done in 0.031s Computing adjusted_mutual_info_score for 10 values of n_clusters and n_samples=100 done in 0.568s Computing mutual_info_score for 10 values of n_clusters and n_samples=100 done in 0.022s Computing adjusted_rand_score for 10 values of n_clusters and n_samples=1000 done in 0.090s Computing v_measure_score for 10 values of n_clusters and n_samples=1000 done in 0.042s Computing adjusted_mutual_info_score for 10 values of n_clusters and n_samples=1000 done in 0.335s Computing mutual_info_score for 10 values of n_clusters and n_samples=1000 done in 0.028s **Python source code:** :download:`plot_adjusted_for_chance_measures.py ` .. literalinclude:: plot_adjusted_for_chance_measures.py :lines: 23- **Total running time of the example:** 1.61 seconds ( 0 minutes 1.61 seconds)