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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Book ChapterDOI
Efficient access methods in deductive and object-oriented databases
TL;DR: The H-tree indexing scheme is shown to be indeed a general access method for the new generation DBMS that most probably will support the object-oriented concept and have the deductive capability and, as an efficient implementation of the semi-naive evaluation of least fixed point computation.
Journal ArticleDOI
Efficiently identifying max-gap clusters in pairwise genome comparison.
TL;DR: The method could identify known operons as well as some novel structures in the genome and it was demonstrated that the current framework for conserved spatial clustering of genes can be used to detect homologous regions in higher organisms, through the comparison of human and mouse genomes.
Proceedings ArticleDOI
Near-imperceptible Neural Linguistic Steganography via Self-Adjusting Arithmetic Coding
Jiaming Shen,Heng Ji,Jiawei Han +2 more
TL;DR: A new linguistic steganography method which encodes secret messages using self-adjusting arithmetic coding based on a neural language model which outperforms the previous state-of-the-art methods on four datasets by 15.3% and 38.9% in terms of bits/word and KL metrics, respectively.
Journal ArticleDOI
Classification and compilation of linear recursive queries in deductive databases
TL;DR: It is demonstrated that based on the graph model all the linear recursive formulas can be classified into a taxonomy of classes and each class shares common characteristics in query compilation and query processing.
Proceedings ArticleDOI
IntruMine: Mining Intruders in Untrustworthy Data of Cyber-physical Systems.
Lu-An Tang,Quanquan Gu,Xiao Yu,Jiawei Han,Thomas F. La Porta,Alice Leung,Tarek Abdelzaher,Lance M. Kaplan +7 more
TL;DR: This study investigates the specific problem of intruder mining in CPS and proposes a method called IntruMine to detect and verify the intruders, which has better effectiveness and efficiency than existing methods.