Clustering algorithms take data and tries to sort it into clusters each with some form of similar characteristics. This is like you might do if looking a scatter graph and noticing that the data falls into two or three groups. Examples of cluster analysis includes k-means which is based around calculating centroids of each cluster and Khonne networks (or aelf-organsing maps) which are a form of neural netwrok where similar data points are classified close to one another a two dimensional grid. Clustering algorithms are a form of unsupevsied learnong, but there are also semi-supervised forms where some or all of the training set has a classification and the clustering algorithm tries to ensure that each cluster has a consistent label, even if there may be several clusters for each label.
Used in Chap. 5: page 68; Chap. 6: pages 84, 91, 93; Chap. 7: pages 100, 101, 102, 106, 108; Chap. 8: pages 117, 123; Chap. 9: pages 134, 139; Chap. 10: pages 147, 151; Chap. 12: page 185; Chap. 18: page 304; Chap. 20: pages 346, 347, 348; Chap. 21: pages 361, 362
Also known as clustering algorithm, clustering algorithms, clusters