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
Mining compressed commodity workflows from massive RFID data sets
TL;DR: This paper proposes a method to construct compressed probabilistic workflows that capture the movement trends and significant exceptions of the overall data sets, but with a size that is substantially smaller than that of the complete RFID workflow.
Patent
Methods and system for mining frequent patterns
TL;DR: In this paper, a frequent pattern tree is used to represent the contents of a database in a manner which is conducive to data mining. But this tree tends to be smaller than the original database and can be mined recursively.
Proceedings Article
A Variance Minimization Criterion to Active Learning on Graphs
Ming Ji,Jiawei Han +1 more
TL;DR: This study considers the problem of active learning over the vertices in a graph, without feature representation, based on the common graph smoothness assumption, which is formulated in a Gaussian random field model and produces a theoretically more robust classifier.
Journal ArticleDOI
Generalization-based data mining in object-oriented databases using an object cube model
TL;DR: The study shows that a set of sophisticated generalization operators can be constructed for generalization of complex data objects, a dimension-based class generalization mechanism can be developed for object cube construction, and sophisticated rule formation methods can be develop for extraction of different kinds of knowledge from data.
Posted ContentDOI
Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning
Xuan Wang,Yu Zhang,Xiang Ren,Yuhao Zhang,Marinka Zitnik,Jingbo Shang,Curtis P. Langlotz,Jiawei Han +7 more
TL;DR: A multi-task learning framework for BioNER to collectively use the training data of different types of entities and improve the performance on each of them, which achieves substantially better performance than state-of-the-art BioNER systems and baseline neural sequence labeling models.