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K-Means Clustering

1 post · Oct 2023

What this trail covers

Hex has evolved its data analysis capabilities by providing a comprehensive guide to K-Means clustering. This includes detailing the algorithm's fundamentals, how it works through initialization, assignment, and iterative updates, and key parameters like the number of clusters (K). The post demonstrates practical application using the Iris dataset, covering data loading, cleaning, transformation (scaling and PCA), and methods for selecting an optimal K value such as the Elbow method and Silhouette Score. It aims to empower users to uncover patterns in unlabeled data and categorize datasets into actionable segments.

All Hex trails
  1. Unveiling patterns in unlabeled data with k-means clusteringThis post introduces and details the K-Means clustering algorithm for Hex users. It explains the core concepts, step-by-step process (initialization, assignment, updating centroids, iteration), and critical parameters (K, initialization methods). The post provides a practical walkthrough using the Iris dataset, including code examples for dependency installation (pandas, numpy, matplotlib, scikit-learn), data loading, cleaning, scaling, and dimensionality reduction with PCA. It also elaborates on methods for selecting the optimal 'K' value, specifically the Elbow method and Silhouette Score, with accompanying visualizations and Python code snippets.