Clustering

• Unsupervised learning wherein you try to identify patterns in data without being given a pre-determined set of labels.

• Common clustering algos
• K-Means
• Hierarchical

• Clustering considerations / Underlying philosophy
• Stability - class of members should not change if  clustering algo is run multiple times
• Inter-group heterogeneity & intra-group homogeneity is important. 
• Members of different segments/cluster should have distinctly different behaviour/traits
• Members of the same cluster/segment should have similar behaviour/trait.

• K-Means
• Customer Segmentation (typical criteria)
• Behavioral (based on customer actions)
• Attitudinal (based on customer intention e.g. brand-consciousness)
• Demographic (could be a good substitute/shortcut for Behavioral)
• Common practice of customer clustering is to use RFM as a triad of features on which to base clustering
• R - Recency (how recent have been a customer's purchases/interactions)
• F - Frequency (frequency of buying)
• M - Monetary value of purchases.
• Other segmentation practices
• RPI 
• Relationship  
• Persona (e.g gift-giver based on the fact that a person orders and mostly ships to other addresses)
• Intention (can be discerned based on browsing pattern)
• CDJ ( Consumer Decision Journey)
• Use a "Funnel" structure
• Hierarchical
• No need to pre-determine number of clusters
• Start with each point being a cluster and then iterate through to form one big cluster
• (Needs more processing power since it is more time-consuming)
• In the process create a dendrogram of the points at each step of cluster
• Clusters meeting at a higher point are more dissimilar
• Determine number of clusters by drawing a horizontal cut-off point across the dendrogram. Number of intersection points gives the # of clusters
• Cut-off line is somewhat arbitrary. Can be done bottom-up, called, agglomerative or top-down, called divisive.
• Linkage is the distance between points of one cluster to another in the process of fusing and creating clusters
• Single-linkage (take min distance between inter-cluster points)
• Complete-linkage (take max distance b/w inter-cluster points)
• Average-linkage (take avg distance b/w inter-cluster points)
• Clustering Choice Considerations