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.
By:
Gordon M Redwine Imprint: Gordon M. Redwine Dimensions:
Height: 229mm,
Width: 152mm,
Spine: 9mm
Weight: 231g ISBN:9783427330684 ISBN 10: 3427330680 Pages: 168 Publication Date:02 May 2023 Audience:
General/trade
,
ELT Advanced
Format:Paperback Publisher's Status: Active