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 ArticleDOI
Diagnostic powertracing for sensor node failure analysis
Mohammad Maifi Hasan Khan,Hieu Le,Michael LeMay,Parya Moinzadeh,Lili Wang,Yong Yang,Dong Kun Noh,Tarek Abdelzaher,Carl A. Gunter,Jiawei Han,Xin Jin +10 more
TL;DR: This paper introduces the tele-diagnostic powertracer, an in-situ troubleshooting tool that uses external power measurements to determine the internal health condition of an unresponsive host and the most likely cause of its failure.
Proceedings ArticleDOI
AC-Close: Efficiently Mining Approximate Closed Itemsets by Core Pattern Recovery
TL;DR: An efficient algorithm AC-Close is proposed to recover the approximate closed itemsets from "core patterns" by focusing on the so-called core patterns, integrated with a top-down mining and several effective pruning strategies, which narrows down the search space to those potentially interesting ones.
Proceedings ArticleDOI
NetTaxo: Automated Topic Taxonomy Construction from Text-Rich Network
TL;DR: This paper proposes NetTaxo, a novel automatic topic taxonomy construction framework, which goes beyond the existing paradigm and allows text data to collaborate with network structure and learns term embeddings from both text and network as contexts.
Journal ArticleDOI
Mining Trajectory Data and Geotagged Data in Social Media for Road Map Inference
TL;DR: A UGC‐based automatic road map inference method is proposed that applies data mining techniques and natural language processing tools to extract not only spatial information – the location of the road network – but also attribute information – road class and road name – in an effort to create a complete road map.
Proceedings ArticleDOI
adaQAC: Adaptive Query Auto-Completion via Implicit Negative Feedback
TL;DR: A novel adaptive model adaQAC is proposed that adapts query auto-completion to users' implicit negative feedback towards unselected query suggestions, and empirical results show that implicitnegative feedback significantly and consistently boosts the accuracy of the investigated static QAC models that only rely on relevance scores.