Given two data items, we often need to calculate some measure or metric of how similar they are. For example, this may be used by a clustering algorithm. For discrete valued features this might simply be a count of how many features are identical. For continuous valued feature some distance measure may be used, such as Euclidean distance or Manhatten block distance, but to be a {\em similarity} measure this would usually be inverted in some way (e.g. 1/distance).
Used in Chap. 7: pages 91, 92; Chap. 8: page 108; Chap. 9: page 118; Chap. 10: pages 134, 141; Chap. 12: page 185; Chap. 16: pages 240, 242, 248; Chap. 18: page 286; Chap. 21: page 340
Also known as similarity, similarity metrics