Use-Cases and Applications of K-Modes Clustering
K-Modes clustering is particularly useful in various domains where categorical data is prevalent:
- Market Segmentation: Grouping customers based on categorical attributes like preferences, buying behavior, and demographics.
- Healthcare: Clustering patients based on categorical medical records to identify patterns in diseases and treatments.
- Social Media Analysis: Categorizing users based on their activity, interests, and interactions.
Revealing K-Modes Cluster Features with Scikit-Learn
Clustering is a powerful technique in unsupervised machine learning that helps in identifying patterns and structures in data. While K-Means is widely known for clustering numerical data, K-Modes is a variant specifically designed for categorical data. In this article, we will delve into the K-Modes algorithm, its implementation using Scikit-Learn, and how to reveal cluster features effectively.
Table of Content
- Understanding K-Modes Clustering
- Implementing K-Modes Clustering with Scikit-Learn
- Use-Cases and Applications of K-Modes Clustering
- Tips for Effective K-Modes Clustering