Cluster analysis can be performed using various techniques and tools. Here are the general steps involved:
Data Collection: Gather data on subscriber behaviors, preferences, demographics, and other relevant factors. Data Preprocessing: Clean and normalize the data to prepare it for analysis. Selection of Clustering Algorithm: Choose an appropriate clustering algorithm such as K-Means, Hierarchical Clustering, or DBSCAN. Model Training: Apply the chosen algorithm to the prepared data to identify clusters. Cluster Validation: Evaluate the quality of the clusters using metrics like silhouette score or Davies-Bouldin index. Interpretation: Analyze and interpret the clusters to understand the characteristics of each group.