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
Relation strength-aware clustering of heterogeneous information networks with incomplete attributes
TL;DR: Zhang et al. as mentioned in this paper designed a probabilistic model which clusters the objects of different types into a common hidden space, by using a user-specified set of attributes, as well as the links from different relations.
Journal ArticleDOI
Constrained frequent pattern mining: a pattern-growth view
Jian Pei,Jiawei Han +1 more
TL;DR: The principles of pattern-growth methods for constrained frequent pattern mining and sequential pattern mining are overviewed and many tough constraints which cannot be handled by previous methods can be pushed deep into the pattern- growth mining process are explored.
Proceedings Article
ROAM: Rule- and Motif-Based Anomaly Detection in Massive Moving Object Data Sets.
TL;DR: This study proposes a new framework named ROAM (Ruleand Motif-based Anomaly Detection in Moving Objects), and develops a general-purpose, rulebased classifier which explores the structured feature space and learns effective rules at multiple levels of granularity.
Book ChapterDOI
Star-cubing: computing iceberg cubes by top-down and bottom-up integration
TL;DR: A new method is presented, Star-Cubing, that integrates the strengths of the previous three algorithms and performs aggregations on multiple dimensions simultaneously and enables the pruning of the group-by's that do not satisfy the iceberg condition.
Journal ArticleDOI
Data mining for Web intelligence
TL;DR: How data mining holds the key to uncovering and cataloging the authoritative links, traversal patterns, and semantic structures that will bring intelligence and direction to the authors' Web interactions is considered.