Classic Density-Based Clustering Algorithm OPTICS with MATLAB Implementation
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Resource Overview
Classic density-based clustering algorithm OPTICS implemented in MATLAB, featuring clear explanations and executable code for practical data analysis applications
Detailed Documentation
The classic OPTICS (Ordering Points To Identify the Clustering Structure) algorithm is a density-based clustering method that extends DBSCAN by creating a reachability plot to visualize cluster hierarchies. This MATLAB implementation provides a practical approach for cluster analysis of datasets, featuring intuitive code structure and straightforward execution. The algorithm employs key functions such as computeReachabilityDistance() and extractDBSCANClustering() to handle large-scale datasets efficiently while maintaining robustness against noise and outliers. The implementation utilizes MATLAB's vectorization capabilities for optimized performance, making it particularly useful for various data analysis and mining tasks. The code includes parameter tuning options for epsilon (neighborhood radius) and MinPts (minimum points threshold), allowing users to adapt the clustering sensitivity to different dataset characteristics.
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