Interesting patterns for clustering high-dimensional data

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.