Understanding K-Modes Clustering
K-Modes clustering is an extension of the K-Means algorithm tailored for categorical data. Unlike K-Means, which uses Euclidean distance, K-Modes employs a simple matching dissimilarity measure. The algorithm iteratively assigns data points to clusters based on the mode (most frequent category) of the cluster.
Key Concepts
- Dissimilarity Measure: K-Modes uses the Hamming distance, which counts the number of mismatches between categorical attributes.
- Cluster Centroids: Instead of mean values, K-Modes uses modes (most frequent categories) as cluster centroids.
- Cluster Assignment: Data points are assigned to the cluster with the nearest mode.
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