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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Journal ArticleDOI
Efficient mining of intertransaction association rules
TL;DR: The notion of intertransaction association rule is introduced and an efficient algorithm, FITI (first intra then inter), is developed for mining intertransactions associations, which adopts two major ideas: an intertransACTION frequent itemset contains only the frequent itemsets of its corresponding intratransaction counterpart.
Book ChapterDOI
Selective Materialization: An Efficient Method for Spatial Data Cube Construction
TL;DR: A spatial data warehouse model, which consists of both spatial and nonspatial dimensions and measures, is proposed, and several strategies proposed, including approximation and partial materialization of the spatial objects resulted from spatial OLAP operations are proposed.
Book ChapterDOI
RecTree: An Efficient Collaborative Filtering Method
TL;DR: An efficient collaborative filtering method, called RecTree (which stands for RECommendation Tree), that addresses the scalability problem with a divide-and-conquer approach and outperforms the well-known collaborative filter, CorrCF, in both execution time and accuracy.
Book ChapterDOI
Constraint-based clustering in large databases
TL;DR: In this article, a scalable constrained clustering algorithm is developed which starts by finding an initial solution that satisfies user-specified constraints and then refines the solution by performing confined object movements under constraints.
Proceedings ArticleDOI
Modeling hidden topics on document manifold
TL;DR: This paper proposes a novel algorithm called Laplacian Probabilistic Latent Semantic Indexing (LapPLSI) for topic modeling, which models the document space as a submanifold embedded in the ambient space and directly performs the topic modeling on this document manifold in question.