Test results and comparison of different cluster solutions.

[quebulm]

This issue concerns grouping the results of different cluster solutions and comparing them. Child Issue for #470

[quebulm]

Pycaret

Clustering Example Output :

   Feature_1  Feature_2    Cluster
0  -9.297688   6.473679  Cluster 3
2  -1.686653   7.793442  Cluster 1
3  -7.097308  -5.781333  Cluster 0

Clustering Modelle

# Auflisten aller verfügbaren Clustering-Modelle
available_models = models()
print(available_models)

#Ausgabe
ID                                            
kmeans                   K-Means Clustering   
ap                     Affinity Propagation   
meanshift             Mean Shift Clustering   
sc                      Spectral Clustering   
hclust             Agglomerative Clustering   
dbscan     Density-Based Spatial Clustering   
optics                    OPTICS Clustering   
birch                      Birch Clustering  

Anomaly Detection

Example Output :

     CaNA_N  Genotype  Treatment  Behavior   class  Anomaly  Anomaly_Score  
0  1.675652   Control  Memantine       C/S  c-CS-m        0      -0.009763  
1  1.743610   Control  Memantine       C/S  c-CS-m        0      -0.010931  
2  1.926427   Control  Memantine       C/S  c-CS-m        0      -0.009985 

PyCaret uses the PyOd library for anomaly detection. The anomaly score of an input sample is computed based on different detector algorithms. For consistency, outliers are assigned with larger anomaly scores. Wow it is calculated depends on the algorithm used for anomaly detection. Check out the documentation stackoverflow