The Fourth International Workshop on Mining Multiple Information Sources (MMIS) Program Committee

The Fourth International Workshop on

Mining Multiple Information Sources

In conjunction with The IEEE International Conference on Data Mining (ICDM 2010)

December 14 2010, Sydney, Australia

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Workshop Program

8: 50 AM – 9:00 AM	Welcome & Opening Remarks
9:00AM – 10:00AM	Keynote Speech Computational and Algorithmic Challenges for the Square Kilometre Array in Imaging Astronomy Dr. Tim Cornwell Astronomy and Space Science Division, CSIRO, Australia
10:00AM – 10: 20 AM	Coffee Break
10:20AM – 12:00PM	Section I: [20 minutes/each] Differential Analysis on Deep Web Data Sources Tantan Liu, Jiedan Zhu, Fan Wang, and Gagan Agrawal Ohio State University, USA Learning Document Labels from Enriched Click Graphs Lan Nie Yahoo! Labs, USA Mining research topics evolving over time using a diachronic multi-source approach Jean-Charles Lamirel INRIA TALARIS team LORIA, France Multiple Feature-based Classifier and Its Application to Image Classification Dong-Chul Park Myong Ji University, Republic of Korea Learning a Combination of Dissimilarities from a Set of Equivalence Constraints Manuel Martin-Merino¹ and Javier De Las Rivas² ¹University Pontificia of Salamanca, Spain ² Cancer Research Center of Salamanca, Spain
12:20PM-1:30PM	Lunch Break [Marigold Restaurant: http://www.marigold.com.au]
1:30PM – 2:30PM	Keynote Speech Learning from Heterogeneous Data Sources Prof. Philip S. Yu University of Illinois at Chicago, USA
2:30PM – 3:30PM	Section II [20 minutes /each] W-LEACH: Weighted Low Energy Adaptive Clustering Hierarchy Aggregation Algorithm for Data Streams in Wireless Sensor Networks Hanady Abdulsalam and Layla Kamel Kuwait University, Kuwait PTCR-Miner: Progressive Temporal Class Rule Mining for Multivariate Temporal Data Classification Chao-Hui Lee and Vincent. S. Tseng National Cheng Kung University, Tainan, Taiwan Event Data Mining and Classification from Multiple Streaming Sources Ashit Talukder Jet Propulsion Laboratory, California Institute of Technology, USA
3:30PM – 4:00PM	Coffee Break
4:00PM – 5:20PM	Section III [20 minutes/each] A Triple-Random Ensemble Classification Method for Mining Multi-label Data Gulisong Nasierding Deakin University, Australia CUBS:Multivariate Sequence Classification Using Bounded Z-score with Sampling Ariella Richardson, Gal Kaminka, and Sarit Kraus Bar Ilan University, Israel Distributed classification on peers with variable data spaces and distributions Vivekanand Gopalkrishnan Nanyang Technological University, Singapore Diffusion Maps: A Superior Semantic Method to Improve Similarity Join Performance Bilal Hawashin¹, Farshad Fotouhi¹, and William Grosky² ¹ Wayne State University, USA ² University of Michigan - Dearborn, USA
5: 20 PM – 5:30 PM	Discussion, Concluding & Remarks