Recent advances in data mining allow for exploiting patterns as the primary means for clustering and classifying large collections of data. In this thesis, we present three advances in pattern-based clustering technology, an advance in semi-supervised pattern-based classification, and a related advance in pattern frequency counting. In our first contribution, we analyze numerous deficiencies with traditional patternsignificance measures such as support and confidence, and propose a web image clustering algorithm that uses an objective interestingness measure to identify significant patterns, yielding measurably better clustering quality.