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CIKM
2010
Springer
14 years 9 months ago
Mining topic-level influence in heterogeneous networks
Influence is a complex and subtle force that governs the dynamics of social networks as well as the behaviors of involved users. Understanding influence can benefit various applic...
Lu Liu, Jie Tang, Jiawei Han, Meng Jiang, Shiqiang...
ICDM
2008
IEEE
184views Data Mining» more  ICDM 2008»
15 years 6 months ago
Bayesian Co-clustering
In recent years, co-clustering has emerged as a powerful data mining tool that can analyze dyadic data connecting two entities. However, almost all existing co-clustering techniqu...
Hanhuai Shan, Arindam Banerjee
ICDM
2006
IEEE
164views Data Mining» more  ICDM 2006»
15 years 5 months ago
Unsupervised Learning of Tree Alignment Models for Information Extraction
We propose an algorithm for extracting fields from HTML search results. The output of the algorithm is a database table– a data structure that better lends itself to high-level...
Philip Zigoris, Damian Eads, Yi Zhang
SDM
2009
SIAM
138views Data Mining» more  SDM 2009»
15 years 9 months ago
ShatterPlots: Fast Tools for Mining Large Graphs.
Graphs appear in several settings, like social networks, recommendation systems, computer communication networks, gene/protein biological networks, among others. A deep, recurring...
Ana Paula Appel, Andrew Tomkins, Christos Faloutso...
DMIN
2008
158views Data Mining» more  DMIN 2008»
15 years 1 months ago
Mining User Movement Behavior Patterns in a Mobile Service Environment
Abstract - The rapid advances of wireless and web technologies enable the mobile users to request various kinds of services via mobile devices at anytime and anywhere. To provide t...
Yen-Ssu Chou, Tzung-Shi Chen