Clustering Principle: Feature Extraction and Similarity Measures

This lecture introduces the concept of clustering, which groups similar data points into clusters. It explains how clustering can be used for feature extraction and data compression, and discusses the importance of intra-cluster and inter-cluster similarity. The lecture also covers the role of prior information in clustering, the impact of outliers, and different similarity measures such as L1-norm and L2-norm. Additionally, it explores the shape of clusters generated by clustering techniques, distinguishing between non-globular and globular clusters.