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.
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Proceedings Article
Adaptive fastest path computation on a road network: a traffic mining approach
TL;DR: An adaptive fastest path algorithm capable of efficiently accounting for important driving and speed patterns mined from a large set of traffic data is presented and it is shown that it provides desirable (short and well-supported) routes, and that it is significantly faster than competing methods.
Proceedings ArticleDOI
A general framework for mining concept-drifting data streams with skewed distributions
TL;DR: This paper proposes a new approach to mine data streams by estimating reliable posterior probabilities using an ensemble of models to match the distribution over under-samples of negatives and repeated samples of positives and formally shows some interesting and important properties of the proposed framework.
Proceedings ArticleDOI
On community outliers and their efficient detection in information networks
TL;DR: This paper proposes an efficient solution by modeling networked data as a mixture model composed of multiple normal communities and a set of randomly generated outliers, and applies the model on both synthetic data and DBLP data sets to demonstrate importance of this concept, as well as the effectiveness and efficiency of the proposed approach.
Journal ArticleDOI
Constraint-based sequential pattern mining: the pattern-growth methods
Jian Pei,Jiawei Han,Wei Wang +2 more
TL;DR: This study shows that constraints can be effectively and efficiently pushed deep into the sequential pattern mining under this new framework and can be extended to constraint-based structured pattern mining as well.