Clustering Techniques for Mixed Data Types

Requirements Clarification & Assessment

When approaching the task of clustering a dataset with mixed data types, it's crucial to understand the nature and distribution of the data involved:

1. Data Types:

   • Numerical features: Continuous data that can take any value within a range.
   • Categorical features: Discrete data that represents categories or groups.

2. Objective:

   • Group similar data points together based on both numerical and categorical features.
   • Ensure that the clustering method appropriately handles the mixed data types to produce meaningful clusters.

3. Challenges:

   • Distance metrics like Euclidean or Manhattan are not suitable for categorical data.
   • Need for a method that can handle both numerical and categorical features simultaneously.

4. Performance Metrics:

   • Assess the quality of clustering using appropriate metrics such as silhouette score, Davies-Bouldin index, or within-cluster sum of squares. These metrics should consider the nature of the data and the chosen distance metric.