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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Proceedings ArticleDOI
When will it happen?: relationship prediction in heterogeneous information networks
TL;DR: The link prediction problem is extended to the relationship prediction problem, by systematically defining both the target relation and the topological features, using a meta path-based approach and directly model the distribution of relationship building time with the use of the extracted topological Features.
Proceedings Article
Mining segment-wise periodic patterns in time-related databases
Jiawei Han,Wan Gong,Yiwen Yin +2 more
TL;DR: This study integrates data cube and Apriori data mining techniques for mining segment-wise periodicity in regard to a fixed length period and shows that data cube provides an efficient structure and a convenient way for interactive mining of multiple-level periodicity.
Journal ArticleDOI
A particle-and-density based evolutionary clustering method for dynamic networks
Min-Soo Kim,Jiawei Han +1 more
TL;DR: A density-based clustering method that efficiently finds temporally smoothed local clusters of high quality by using a cost embedding technique and optimal modularity and a mapping method based on information theory that makes sequences of smoothedLocal clusters as close as possible to data-inherent quasi l-KKs.
Journal ArticleDOI
Cancer classification using gene expression data
Ying Lu,Jiawei Han +1 more
TL;DR: This survey paper presents a comprehensive overview of various proposed cancer classification methods and evaluates them based on their computation time, classification accuracy and ability to reveal biologically meaningful gene information.
Book ChapterDOI
Graph regularized transductive classification on heterogeneous information networks
TL;DR: This paper considers the transductive classification problem on heterogeneous networked data which share a common topic and proposes a novel graph-based regularization framework, GNetMine, to model the link structure in information networks with arbitrary network schema and arbitrary number of object/link types.