Table 2. Clustering evaluation on churn prediction datasets.

Technique	Accuracy	Recall	Precision	F-measure	RMSE	MSE	MAE
Clustering evaluation on GitHub churn prediction dataset
X-means	50.58	52.05	14.72	22.94	0.78	0.6084	0.15
K-means	50.58	52.05	14.72	22.94	0.78	0.6084	0.15
K-med	65.44	29.13	14.37	19.25	0.69	0.4761	0.11
Random	50.96	48.93	14.19	22.01	0.75	0.5625	0.14
Clustering Evaluation on Bigml churn prediction dataset
X-means	50.04	48.86	14.26	22.08	0.63	0.3969	0.0992
K-means	50.04	48.86	14.26	22.08	0.63	0.3969	0.0992
K-med	55.56	41.82	14.40	21.43	0.54	0.2916	0.0729
Random	50.94	49.06	14.57	22.47	0.61	0.3721	0.093