Clustering and Careful Interpretation of Mining Results

Examine clustering as a major unsupervised task, including similarity matrices, algorithms such as K-means and DBSCAN, computational considerations like spatial access methods, and cautions about finding apparent patterns in random data and making many statistical tests without inflating errors.