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 99, 100; Chap. 8: page 117; Chap. 9: pages 127, 128; Chap. 10: pages 145, 152, 153; Chap. 12: page 200; Chap. 16: pages 259, 261, 267; Chap. 18: page 306; Chap. 21: page 361
Also known as similarity, similarity metrics