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
P-Rank: a comprehensive structural similarity measure over information networks
TL;DR: A new similarity measure, P-Rank (Penetrating Rank), toward effectively computing the structural similarities of entities in real information networks and a fixed point algorithm to reinforce structural similarity of vertex pairs beyond the localized neighborhood scope toward the entire information network is proposed.
Journal ArticleDOI
Survey on social tagging techniques
TL;DR: Different techniques employed to study various aspects of tagging are summarized, including properties of tag streams, tagging models, tag semantics, generating recommendations using tags, visualizations of tags, applications of tags and problems associated with tagging usage.
Proceedings ArticleDOI
Summarizing itemset patterns: a profile-based approach
TL;DR: This paper examines how to summarize a collection of itemset patterns using only K representatives, a small number of patterns that a user can handle easily, and proposes a quality measure function to determine the optimal value of parameter K.
Journal ArticleDOI
Frequent Closed Sequence Mining without Candidate Maintenance
Jianyong Wang,Jiawei Han,Chun Li +2 more
TL;DR: BIDE is presented, an efficient algorithm for mining frequent closed sequences without candidate maintenance that adopts a novel sequence closure checking scheme called Bl-Directional Extension and prunes the search space more deeply compared to the previous algorithms by using the BackScan pruning method.
Proceedings ArticleDOI
CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases
TL;DR: CoType as mentioned in this paper proposes a domain-independent framework that runs a data-driven text segmentation algorithm to extract entity mentions and jointly embeds entity mentions, relation mentions, text features and type labels into two low-dimensional spaces, where objects whose types are close will also have similar representations.