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oai:www.ideals.illinois.edu:2142/81747 2023-07-11 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

Han, Jiawei Yan, Xifeng 2015-09-25T20:20:15Z 2015-09-25T20:20:15Z 10000-01-01 2006 2006 The developed concepts, theories, and systems may significantly deepen the understanding of data mining principles in structural pattern discovery, interpretation and search. The formulation of a general graph information system through this study could provide fundamental supports to graph-intensive applications in multiple domains. Made available in DSpace on 2015-09-25T20:20:15Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3243031.pdf: 4537089 bytes, checksum: 488c80b55bb63151c45a797cd1aed088 (MD5) Previous issue date: 2006 Embargo set by: Seth Robbins for item 83028 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs U of I Only 157 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006. http://hdl.handle.net/2142/81747 (MiAaPQ)AAI3243031 eng Computer Science Mining, Indexing and Similarity Search in Large Graph Data Sets text Computer Science Computer Science University of Illinois at Urbana-Champaign Dissertation Ph.D.