J
Jiawei Han
Researcher at University of Illinois at Urbana–Champaign
Publications - 1302
Citations - 155054
Jiawei Han is an academic researcher from University of Illinois at Urbana–Champaign. The author has contributed to research in topics: Cluster analysis & Knowledge extraction. The author has an hindex of 168, co-authored 1233 publications receiving 143427 citations. Previous affiliations of Jiawei Han include Georgia Institute of Technology & United States Army Research Laboratory.
Papers
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Proceedings ArticleDOI
SOBER: statistical model-based bug localization
TL;DR: The result demonstrated the power of the approach in bug localization: SOBER can help programmers locate 68 out of 130 bugs in the Siemens suite when programmers are expected to examine no more than 10% of the code, whereas the best previously reported is 52 out of130.
Journal ArticleDOI
Classification and Novel Class Detection in Concept-Drifting Data Streams under Time Constraints
TL;DR: This work proposes a data stream classification technique that integrates a novel class detection mechanism into traditional classifiers, enabling automatic detection of novel classes before the true labels of the novel class instances arrive.
Journal ArticleDOI
Mining multiple-level association rules in large databases
Jiawei Han,Yongjian Fu +1 more
TL;DR: The study shows that efficient algorithms can be developed from large databases for the discovery of interesting and strong multiple-level association rules from large transaction databases.
Journal ArticleDOI
On graph query optimization in large networks
Peixiang Zhao,Jiawei Han +1 more
TL;DR: The experimental studies demonstrate the effectiveness and scalability of SPath, which proves to be a more practical and efficient indexing method in addressing graph queries on large networks.
Journal ArticleDOI
Mining coherent dense subgraphs across massive biological networks for functional discovery
TL;DR: A novel algorithm is developed, CODENSE, to efficiently mine frequent coherent dense subgraphs across large numbers of massive graphs and is scalable in the number and size of the input graphs and adjustable in terms of exact or approximate pattern mining.