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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

RelSim: Relation Similarity Search in Schema-Rich Heterogeneous Information Networks

TL;DR: A new meta-path-based relation similarity measure, RelSim, is defined, to measure the similarity between relation instances in schema-rich HINs, and an optimization model is proposed to efficiently learn LSR implied in the query through linear programming.
Book ChapterDOI

Warehousing and mining massive RFID data sets

TL;DR: In this article, the authors propose two data models for the management of RFID data, a path cube that preserves object transition information while allowing muti-dimensional analysis of path dependent aggregates and a workflow cube that summarizes the major patterns and significant exceptions in the flow of items through the system.
Proceedings ArticleDOI

Inferring human mobility patterns from taxicab location traces

TL;DR: It is shown that while past approaches are effective in detecting hotspots using location traces, they are largely ineffective in identifying trips (pairs of pickup and dropoff points) and proposed the use of a graph theory concept - stretch factor in a novel manner to identify trip(s) made by a taxicab.

K -Medoids Clustering.

Xin Jin, +1 more
Proceedings ArticleDOI

Node, motif and subgraph: leveraging network functional blocks through structural convolution

TL;DR: NEST is developed, a novel hierarchical network embedding method combining motif filtering and convolutional neural networks that enables NEST to capture exact small structures within networks, and convolved embedding allows it to fully explore complex substructures and their combinations.