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
Automatic construction and ranking of topical keyphrases on collections of short documents
TL;DR: A framework for topical keyphrase generation and ranking, based on the output of a topic model run on a collection of short documents, is introduced, able to directly compare and rank phrases of different lengths.
Proceedings Article
Mining Multi-Dimensional Constrained Gradients in Data Cubes
TL;DR: An efficient algorithm is developed, which pushes constraints deep into the computation process, finding all gradient-probe cell pairs in one pass, and explores bi-directional pruning between probe cells and gradient cells, utilizing transformed measures and dimensions.
Book ChapterDOI
Association Mining in Large Databases: A Re-examination of Its Measures
Tianyi Wu,Yuguo Chen,Jiawei Han +2 more
TL;DR: This work re-examine the null-invariant measures and finds interestingly that they can be expressed as a generalized mathematical mean, and there exists a total ordering of them.
Journal ArticleDOI
Making SVMs Scalable to Large Data Sets using Hierarchical Cluster Indexing
TL;DR: This paper presents a method, Clustering-Based SVM (CB-SVM), that maximizes the SVM performance for very large data sets given a limited amount of resource, e.g., memory, and applies a hierarchical micro-clustering algorithm that scans the entire data set only once to provide an SVM with high quality samples.
Spectral Regression for Dimensionality Reduction
Deng Cai,Xiaofei He,Jiawei Han +2 more
TL;DR: Spectral Regression casts the problem of learning an embedding function into a regression framework, which avoids eigen-decomposition of dense matrices and can be performed in supervised, unsupervised and semisupervised situation.