Cheat sheet for implementing 7 methods for selecting the optimal number

Cheat sheet for implementing 7 methods for selecting the optimal number

Select the optimal number of clusters based on multiple clustering validation metrics like Gap Statistic, Silhouette Coefficient, Calinski-Harabasz Index etc.

Segmentation provides a data driven angle for examining meaningful segments that executives can use to take targeted actions and improve business outcomes. Many executives run the risk of making decisions based on overgeneralizations because they utilize a one-size-fits-all approach to assess their business ecosystem. Segmentation, however, improves decision-making by providing multiple meaningful lenses to break apart data and take action.

One of the most perplexing issues we face while trying to segment customers or products is choosing the ideal number of segments. This is a key parameter for multiple clustering algorithms like K means, agglomerative clustering and GMM clustering. Unless our data has just 2 or 3 dimensions, it is not possible to visually understand the clusters present in the data. And in most practical applications, we will have more than 3 dimensions. This blog will help the readers understand and quickly implement the most popular techniques for selecting optimal number of clusters:

  1. Gap Statistic
  2. Elbow Method
  3. Silhouette Coefficient
  4. Calinski-Harabasz Index
  5. Davies-Bouldin Index
  6. Dendrogram
  7. Bayesian information criterion (BIC)

For this exercise, we will be working with clickstream data from an online store offering clothing for pregnant women. It has data from April 2008 to August 2008 and includes variables like product category, location of the photo on the webpage, country of origin of the IP address and product price in US dollars. Before selecting optimal number of clusters, we will need to prepare the data for segmentation.

I encourage you to check out the article below for an in-depth explanation of different steps for preparing data for segmentation before proceeding further:

One Hot Encoding, Standardization, PCA: Data preparation for segmentation in python

ai segmentation clustering python machine-learning

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