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
Ranking-based classification of heterogeneous information networks
TL;DR: A novel ranking-based iterative classification framework that generates more accurate classes than the state-of-art classification methods on networked data, but also provides meaningful ranking of objects within each class, serving as a more informative view of the data than traditional classification.
Journal ArticleDOI
Optimization of constrained frequent set queries with 2-variable constraints
TL;DR: A notion of quasi-succinctness is introduced, which allows a quasi-Succinct 2-var constraint to be reduced to two succinct 1-var constraints for pruning, and a query optimizer is proposed that is ccc-optimal, i.e., minimizing the effort incurred w.r.t. constraint checking and support counting.
Proceedings ArticleDOI
Classification of software behaviors for failure detection: a discriminative pattern mining approach
TL;DR: This work addresses software reliability issues by proposing a novel method to classify software behaviors based on past history or runs, and finds that the pattern-based classification technique outperforms the baseline approach by 24.68% in accuracy.
Book ChapterDOI
Fundamentals of spatial data warehousing for geographic knowledge discovery
TL;DR: The penetration of data warehouses into the management and exploitation of spatial databases is a major trend as it is for non-spatial databases.
Book ChapterDOI
Community mining from multi-relational networks
TL;DR: This paper systematically analyzes the problem of mining hidden communities on heterogeneous social networks and proposes a new method for learning an optimal linear combination of these relations which can best meet the user's expectation.