KDD 2007 Workshop on

Mining Multiple Information Sources

In conjunction with
The 13th International Conference on Knowledge Discovery and Data Mining
(KDD 2007)

August 12-15, 2007, San Jose, CA USA


[Call For Papers]  [Accepted Papers] [Workshop Program]  [Program Committee]

Workshop Proceedings [pdf]

Section 1 (9:00AM – 10:05 AM)    

Ye Tian, Gary Weiss, D. Frank Hsu, and Qiang Ma

Department of Computer and Information Science, Fordham University, USA

  • 9:25 AM 9:50AM: Mining Vector-Item Patterns for Annotating Protein Domains Paper

Jianfei Wu and Anne M. Denton

Department of Computer Science, North Dakota State University, USA

  • 9:50AM 10:05 AM: Combining Mining Results from Multiple Sources in Clinical Trials and Microarray Applications

Fatih Altiparmak1, Ozgur Ozturk1, Selnur Erdal2, Hakan Ferhatosmanoglu1, and Donald C. Trost3

1 Department of Computer Science and Engineering, the Ohio State University, USA

2 The Ohio State University Medical Center

3 Pfizer Inc., Global Research and Development, USA

Invited Talk (10:20AM – 11:00AM)    Top

  • Functions, Networks, and Phenotypes by Integrative Genomics Analysis

Xianghong Jasmine Zhou

Department of Molecular and Computational Biology, University of Southern California, USA


Section 2 (11:00AM – 12:05AM)    Top

  • 11:00AM 11:25AM: Finding New Customers Using Unstructured and Structured Data

Prem Melville, Yan Liu, Richard Lawrence, Ildar Khabibrakhmanov, Cezar Pendus,

and Timothy Bowden

IBM T.J. Watson Research Center, USA

  • 11:25AM 11:50AM: Incorporating Background Knowledge from the World Wide Web for Rule Evaluation using the Minimum Discriminative Information Principle

Samah Jamal Fodeh and Pang-Ning Tan

Department of Computer Science, Michigan State University, USA

  • 11:50AM 12:05AM: Integrating Projects from Multiple Open Source Code Forges

Megan Conklin

Elon University, USA


Due to the visa problem, the authors of the following paper are not able to present their work

  • An Ensembled based Bayesian Network Learning Algorithm on Limited Data

Feng Liu1, Fengzhan Tian2, and Qiliang Zhu1

1 Beijing University of Posts and Telecommunications, China

2 Beijing Jiaotong University, China