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
More filters
Proceedings ArticleDOI
On Efficient Processing of Subspace Skyline Queries on High Dimensional Data
TL;DR: Methods for answering subspace skyline query on high dimensional data such that both prematerialization storage and query time can be moderated are proposed.
Journal ArticleDOI
Learning in relational databases: an attribute-oriented approach
TL;DR: The method adopts the artificial intelligence “learning‐from‐examples” paradigm and applies in the learning process an attribute‐oriented concept tree ascending technique which integrates database operations with thelearning process and provides a simple and efficient way of learning from databases.
Proceedings ArticleDOI
Did You Enjoy the Ride? Understanding Passenger Experience via Heterogeneous Network Embedding
TL;DR: Based on in-depth analysis of large-scale travel data from a popular taxicab platform in China, PHINE (Pattern-aware Heterogeneous Information Network Embedding) is developed for data-driven user experience modeling and delivers high-quality predictions for passenger satisfaction on a daily basis.
Reference BookDOI
Machine Learning and Knowledge Discovery for Engineering Systems Health Management
Ashok N. Srivastava,Jiawei Han +1 more
TL;DR: Reflecting the interdisciplinary nature of the field, this book shows how various machine learning and knowledge discovery techniques are used in the analysis of complex engineering systems to maintain a high degree of reliability.
Proceedings Article
Fast distributed algorithm for mining association rules
TL;DR: An interesting distributed association rule mining algorithm, FDM (fast distributed mining of association rules), which generates a small number of candidate sets and substantially reduces the number of messages to be passed at mining association rules is proposed.