Clustering Multidimensional Spatial Datasets With DBSCAN, OPTICS, BIRCH, K-Means, and Two-Step Methods: A comparative Evaluation of Five Algorithms

This book presents a systematic machine learning approach to density estimation and clustering of multidimensional real-life spatial datasets, utilizing density-based clustering methods, DBSCAN, and its extension, OPTICS. It compares their clustering performance to that of the traditional centroid-based K-means clustering algorithm, the hierarchical BIRCH clustering algorithm combined with the centroid-based K-means, and the hybrid two-step clustering algorithm (a combination of hierarchica...