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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Book
Data Mining: Know It All
Soumen Chakrabarti,Earl Cox,Eibe Frank,Ralf Hartmut Gting,Jiawei Han,Xia Jiang,Micheline Kamber,Sam Lightstone,Tom Nadeau,Richard E. Neapolitan,Dorian Pyle,Mamdouh Refaat,Markus Schneider,Toby J. Teorey,Ian H. Witten +14 more
TL;DR: A quick and efficient way to unite valuable content from leading data mining experts, thereby creating a definitive, one-stop-shopping opportunity for customers to receive the information they would otherwise need to round up from separate sources.
Proceedings ArticleDOI
Parallel mining of closed sequential patterns
TL;DR: An algorithm, called Par-CSP (Parallel Closed Sequential Pattern mining), to conduct parallel mining of closed sequential patterns on a distributed memory system by exploiting the divide-and-conquer property so that the overhead of interprocessor communication is minimized.
Posted Content
Empower Sequence Labeling with Task-Aware Neural Language Model
TL;DR: This article proposed a novel neural framework to extract abundant knowledge hidden in raw texts to empower the sequence labeling task, where character-aware neural language models are incorporated to extract character-level knowledge.
Proceedings ArticleDOI
Spectral regression: a unified subspace learning framework for content-based image retrieval
Deng Cai,Xiaofei He,Jiawei Han +2 more
TL;DR: By using techniques from spectral graph embedding and regression, this paper proposes a unified framework, called spectral regression, for learning an image subspace, which provides much faster computation and therefore makes the retrieval system capable of responding to the user's query more efficiently.
Journal ArticleDOI
Efficient classification across multiple database relations: a CrossMine approach
TL;DR: This paper proposes a new approach, called CrossMine, which includes a set of novel and powerful methods for multirelational classification, including 1) tuple ID propagation, an efficient and flexible method for virtually joining relations, which enables convenient search among different relations, and new definitions for predicates and decision-tree nodes.