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 60; Chap. 6: pages 75, 81, 84; Chap. 7: pages 92, 93, 98, 100; Chap. 8: pages 108, 109, 115; Chap. 9: pages 124, 129; Chap. 10: pages 135, 136, 140; Chap. 12: page 171; Chap. 18: page 285; Chap. 20: pages 327, 328; Chap. 21: pages 339, 341
Also known as clustering algorithm, clustering algorithms, clusters