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
Visual cube and on-line analytical processing of images
TL;DR: Visual Cube and multi-dimensional OLAP of image collections, such as web images indexed in search engines, product images and photos shared on social networks, are proposed and efficient algorithms are developed to construct Visual Cube.
Proceedings of the Second International Conference on Knowledge Discovery and Data Mining
TL;DR: This report contains papers from the second international conference on knowledge discovery and data mining, which focused on combining data mining and machine learning.
Book ChapterDOI
Knowledge discovery in databases: A rule-based attribute-oriented approach
TL;DR: The background knowledge representation is extended from an unconditional non-rule-based concept hierarchy to a rule- based concept hierarchy, which enhances greatly its representation power.
Proceedings ArticleDOI
Relation Learning on Social Networks with Multi-Modal Graph Edge Variational Autoencoders
TL;DR: This work aims to develop a principled and principled frame- work that can pro le user relations as edge semantics in social networks by integrating multi-modal signals in the presence of noisy and incomplete data.
Journal ArticleDOI
Efficient Keyword-Based Search for Top-K Cells in Text Cube
Bolin Ding,Bo Zhao,Cindy Xide Lin,Jiawei Han,ChengXiang Zhai,Ashok N. Srivastava,Nikunj C. Oza +6 more
TL;DR: This paper defines a keyword-based query language and an IR-style relevance model for scoring/ranking cells in the text cube, and proposes four approaches to solve the problem of keyword search in a data cube with text-rich dimension(s) (so-called text cube): inverted-index one- scan, document sorted-scan, bottom-up dynamic programming, and search-space ordering.