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
Towards Active Learning on Graphs: An Error Bound Minimization Approach
Quanquan Gu,Jiawei Han +1 more
TL;DR: This paper presents a data-dependent error bound for a graph-based learning method, namely learning with local and global consistency (LLGC), and shows that the empirical transductive Rademacher complexity of the function class for LLGC provides a natural criterion for active learning.
Proceedings ArticleDOI
Discriminative Topic Mining via Category-Name Guided Text Embedding
TL;DR: In this article, a new task, discriminative topic mining, is proposed, which leverages a set of user-provided category names to mine discriminating topics from text corpora, which helps a user understand clearly and distinctively the topics he/she is most interested in.
Proceedings ArticleDOI
HiExpan: Task-Guided Taxonomy Construction by Hierarchical Tree Expansion
Jiaming Shen,Zeqiu Wu,Dongming Lei,Chao Zhang,Xiang Ren,Michelle Vanni,Brian M. Sadler,Jiawei Han +7 more
TL;DR: This paper proposes an expansion-based taxonomy construction framework, namely HiExpan, which automatically generates key term list from the corpus and iteratively grows the seed taxonomy and incorporates a weakly-supervised relation extraction module to extract the initial children of a newly-expanded node.
Proceedings ArticleDOI
Cross-relational clustering with user's guidance
TL;DR: This work proposes a new approach, called CrossClus, which performs cross-relational clustering with user's guidance, which takes care of both quality in feature extraction and efficiency in clustering.
Proceedings ArticleDOI
C-Cubing: Efficient Computation of Closed Cubes by Aggregation-Based Checking
TL;DR: A new measure, called closedness, is proposed, for efficient closed data cubing, and it is shown that closedness is an algebraic measure and can be computed efficiently and incrementally.