Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Learn about various distance metrics and their applications in data mining through this recorded university lecture from the University of Utah's Data Science program. Explore fundamental concepts starting with probability-similarity curves and sensitive families before diving into different distance measurements. Master key distance metrics including Jaccard, Euclidean, Manhattan, Lp distances, Mahalanobis, cosine, and angular distances. Understand the mathematical definitions, practical applications, and limitations of each metric type. Gain insights into unit balls, proper usage guidelines, and the relationship between angular distance and Locality-Sensitive Hashing (LSH). The comprehensive coverage provides both theoretical foundations and practical implementation considerations for data mining applications.