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
Learning search tasks in queries and web pages via graph regularization
TL;DR: A novel graph-based regularization algorithm is designed for search task prediction by leveraging the graph to simultaneously classify queries and web pages into the popular search tasks by exploiting their content together with click-through logs.
Proceedings ArticleDOI
Guiding Corpus-based Set Expansion by Auxiliary Sets Generation and Co-Expansion
TL;DR: Set-CoExpan as discussed by the authors proposes a new framework that automatically generates auxiliary sets as negative sets that are closely related to the target set of user's interest, and then performs multiple sets co-expansion that extracts discriminative features by comparing target set with auxiliary sets, to form multiple cohesive sets.
Proceedings ArticleDOI
Selective sampling on graphs for classification
TL;DR: This paper presents an online version of the well-known Learning with Local and Global Consistency method (OLLGC), which is essentially a second-order online learning algorithm, and can be seen as an online ridge regression in the Hilbert space of functions defined on graphs.
Proceedings ArticleDOI
Some performance results on recursive query processing in relational database systems
Jiawei Han,Hongjun Lu +1 more
TL;DR: Both analytical and experimental results indicate that for efficient recursive database processing it is important to apply the following heuristics: performing selection first, making use of wavefront relations, and grouping those joins which reduce the size of intermediate results.
Proceedings ArticleDOI
TopCells: Keyword-based search of top-k aggregated documents in text cube
TL;DR: This paper aims to support keyword search in a data cube with text-rich dimension(s) (so-called text cube) by proposing a relevance scoring model and efficient ranking algorithms.