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
Laplacian Regularized Gaussian Mixture Model for Data Clustering
TL;DR: This paper introduces a regularized probabilistic model based on manifold structure for data clustering, called Laplacian regularized Gaussian Mixture Model (LapGMM), which is modeled by a nearest neighbor graph, and the graph structure is incorporated in the maximum likelihood objective function.
Posted Content
Generalized Fisher Score for Feature Selection
TL;DR: In this article, a generalized Fisher score was proposed to jointly select features, which maximizes the lower bound of traditional Fisher score by solving a quadratically constrained linear programming (QCLP) problem.
Proceedings ArticleDOI
Mining Quality Phrases from Massive Text Corpora
TL;DR: A new framework that extracts quality phrases from text corpora integrated with phrasal segmentation is proposed, which requires only limited training but the quality of phrases so generated is close to human judgment.
Proceedings ArticleDOI
Direct Discriminative Pattern Mining for Effective Classification
TL;DR: A direct discriminative pattern mining approach, DDPMine, is proposed to tackle the efficiency issue arising from the two-step approach and outperforms the state-of-the-art associative classification methods in terms of both accuracy and efficiency.
Journal ArticleDOI
Speed up kernel discriminant analysis
Deng Cai,Xiaofei He,Jiawei Han +2 more
TL;DR: Spectral Regression Kernel Discriminant Analysis is presented, which casts discriminant analysis into a regression framework, which facilitates both efficient computation and the use of regularization techniques.