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
Traffic density-based discovery of hot routes in road networks
TL;DR: A new density-based algorithm named FlowScan is proposed, which is both efficient and effective at discovering hot routes and instead of clustering the moving objects, road segments are clustered based on the density of common traffic they share.
Proceedings ArticleDOI
Can we push more constraints into frequent pattern mining
Jian Pei,Jiawei Han +1 more
TL;DR: Permission to make digital or hard copies of part or all of this work or personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear the full citation on the first page.
Proceedings ArticleDOI
Multi-dimensional sequential pattern mining
TL;DR: This paper examines feasible combinations of efficient sequential pattern mining and multi-dimensional analysis methods, as well as develop uniform methods for high-performance mining, which integrates the multidimensional analysis and sequential data mining.
Proceedings ArticleDOI
Spectral Regression: A Unified Approach for Sparse Subspace Learning
Deng Cai,Xiaofei He,Jiawei Han +2 more
TL;DR: This paper proposes a novel dimensionality reduction framework, called Unified Sparse Subspace Learning (USSL), for learning sparse projections, which casts the problem of learning the projective functions into a regression framework, which facilitates the use of different kinds of regularizes.
Book ChapterDOI
Quotient cube: how to summarize the semantics of a data cube
TL;DR: This paper develops techniques for partitioning cube cells for obtaining succinct summaries, and introduces the quotient cube, which is optimal for monotone aggregate functions and a locally optimal solution for nonmonotone functions.