Basics of hierarchical clustering

Analisi di cluster in Python

Shaumik Daityari

Business Analyst

Creating a distance matrix using linkage

scipy.cluster.hierarchy.linkage(observations, 
                                method='single', 
                                metric='euclidean', 
                                optimal_ordering=False
)

method: how to calculate the proximity of clusters
metric: distance metric
optimal_ordering: order data points

Which method should use?

'single': based on two closest objects
'complete': based on two farthest objects
'average': based on the arithmetic mean of all objects
'centroid': based on the geometric mean of all objects
'median': based on the median of all objects
'ward': based on the sum of squares

Create cluster labels with fcluster

scipy.cluster.hierarchy.fcluster(distance_matrix, 
                                 num_clusters,
                                 criterion
)

distance_matrix: output of linkage() method
num_clusters: number of clusters
criterion: how to decide thresholds to form clusters

Hierarchical clustering with ward method

Hierarchical clustering with single method

Hierarchical clustering with complete method

Final thoughts on selecting a method

No one right method for all
Need to carefully understand the distribution of data

Let's try some exercises

Analisi di cluster in Python

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