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
Graph OLAP: a multi-dimensional framework for graph data analysis
TL;DR: It is argued that it is critically important to OLAP graph structured data and a novel Graph OLAP framework is proposed, and a discovery-driven multi-dimensional analysis model is proposed to ensure that OLAP is performed in an intelligent manner, guided by expert rules and knowledge discovery processes.
Journal ArticleDOI
Pushing support constraints into association rules mining
Ke Wang,Yu He,Jiawei Han +2 more
TL;DR: This work presents a framework of frequent itemset mining in the presence of support constraints, and proposes a strategy to "push" support constraints into the Apriori itemset generation so that the "best" minimum support is determined for each itemset at runtime to preserve the essence of A Priori.
Book ChapterDOI
gPrune: a constraint pushing framework for graph pattern mining
TL;DR: The exploration of these antimonotonicities in the context of graph pattern mining is a significant extension to the known classification of constraints, and deepens the understanding of the pruning properties of structural graph constraints.
Proceedings ArticleDOI
Mining knowledge at multiple concept levels
TL;DR: Methods for mining knowledge at multiple concept levels can often be developed by extension of existing data mining techniques, and it is often necessary to adopt techniques such as step-by-step generalization/specialization or progressive deepening of a knowledge mining process.
Journal ArticleDOI
H-Mine: Fast and space-preserving frequent pattern mining in large databases
TL;DR: This study shows that H-mine has an excellent performance for various kinds of data, outperforms currently available algorithms in different settings, and is highly scalable to mining large databases.