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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.

Papers
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Book ChapterDOI

Advanced Cluster Analysis

TL;DR: This chapter discusses the advanced topics of cluster analysis, including fuzzy clustering and probabilistic model-based clustering, and fundamental methods for cluster analysis on high-dimensional data are introduced.
Posted Content

World Knowledge as Indirect Supervision for Document Clustering

TL;DR: In this article, the authors provide an example of using world knowledge for domain dependent document clustering and propose a clustering algorithm that can cluster multiple types and incorporate the sub-type information as constraints.
Book ChapterDOI

The Level-Cycle Merging Method

TL;DR: Level-Cycle Merging method is an extension of the counting method to process linear recursive queries in both cyclic and acyclic databases and shows that it compares favorably with other recently developed cyclic counting techniques.
Journal ArticleDOI

Two-particle momentum correlations in jets produced in pp̄ collisions at s=1.96TeV

T. Aaltonen, +647 more
- 16 May 2008 - 
TL;DR: In this article, the first measurement of two-particle momentum correlations in jets produced in pp collisions at {radical}(s)=1.96 TeV was obtained for charged particles within a restricted cone with an opening angle of 0.5 radians around the jet axis.
Proceedings ArticleDOI

Hierarchical Metadata-Aware Document Categorization under Weak Supervision

TL;DR: In this paper, a joint representation learning and data augmentation module is proposed for document categorization under weak supervision, which allows simultaneous modeling of category dependencies, metadata information and textual semantics, and introduces a hierarchical synthesizing training documents to complement the original, small-scale training set.