similarity measure

Terms from Artificial Intelligence: humans at the heart of algorithms

Page numbers are for draft copy at present; they will be replaced with correct numbers when final book is formatted. Chapter numbers are correct and will not change now.

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