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
Graph cube: on warehousing and OLAP multidimensional networks
TL;DR: Graph Cube is introduced, a new data warehousing model that supports OLAP queries effectively on large multidimensional networks and is shown to be a powerful and efficient tool for decision support on large multi-dimensional networks.
Posted Content
CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases
TL;DR: A novel domain-independent framework that jointly embeds entity mentions, relation mentions, text features and type labels into two low-dimensional spaces, and adopts a novel partial-label loss function for noisy labeled data and introduces an object "translation" function to capture the cross-constraints of entities and relations on each other.
Proceedings ArticleDOI
Mining long sequential patterns in a noisy environment
TL;DR: The concept of compatibility matrix is introduced as the means to provide a probabilistic connection from the observation to the underlying true value and a new metric match is proposed to capture the "real support" of a pattern which would be expected if a noise-free environment is assumed.
Proceedings ArticleDOI
Mining closed relational graphs with connectivity constraints
TL;DR: One interesting pattern in relational graphs is frequent highly connected subgraph which can identify recurrent groups and clusters in social networks and corresponds to communities where people are strongly associated.
Journal ArticleDOI
Towards on-line analytical mining in large databases
TL;DR: In this article, a data mining system, DBMiner, has been developed for interactive mining of multiple-level knowledge in large relational databases and data warehouses, including characterization, comparison, association, classification, prediction, and clustering.