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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.

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Spectral regression: a regression framework for efficient regularized subspace learning

TL;DR: This thesis introduces a novel dimensionality reduction framework, called Spectral Regression (SR), which casts the problem of learning an embedding function into a regression framework, which avoids eigen-decomposition of dense matrices.
Proceedings Article

On Bayesian interpretation of fact-finding in information networks

TL;DR: This paper presents a new foundation for QoI analysis in information networks, that is of great value in deriving information from unreliable sources, and is validated by extensive simulation where analysis shows significant improvement over past work and great correspondence with ground truth.
Journal ArticleDOI

Predicting future popularity trend of events in microblogging platforms

TL;DR: The problem of predicting future popularity trend of events on microblogging platforms is introduced and regression, classification and hybrid approaches are explored, using a large set of popularity, social and event features, to predict event popularity.
Proceedings ArticleDOI

Generating semantic annotations for frequent patterns with context analysis

TL;DR: A general approach to generate semantic annotations for a frequent pattern by constructing its context model, selecting informative context indicators, and extracting representative transactions and semantically similar patterns is proposed.
Proceedings Article

Join Index Hierarchies for Supporting Efficient Navigations in Object-Oriented Databases

TL;DR: A join index hierarchy method that constructs a hierarchy of join indices and transforms a sequence of pointer chasing operations into a simple search in an appropriate join index file, and thus accelerates navigation in object-oriented databases.