Data Mining Research Papers 2012

Research, USA Myra Spiliopoulou, Otto-von-Guericke-Universitaet Magdeburg, Germany Parthasarathy Srinivasan, Ohio State University Hannu Toivonen, University of Helsinki, Finland Wei Wang, University of North Carolina at Chapel Hill, USA Qiang Yang, Hong Kong University of Technology, Hong Kong Jieping Ye, Arizona State University, USA Bianca Zadrozny, IBM T. Watson Research Center, USA Mohammed Zaki, Rensselaer Polytechnic Institute, USA Program Committee Members Naoki Abe, IBM Research, USA Foto Afrati, National Technical University of Athens, Greece Nitin Agarwal, University of Arkansas at Little Rock, USA Mohammad Al Hasan, Indiana University-Purdue University, USA Aijun An, York University, Canada Tony Bagnall, University of East Anglia, United Kingdom James Bailey, University of Melbourne, Australia Jose Luis Balcazar, Universidad de Cantabria, Spain Daniel Barbara, George Mason University, USA Gustavo Batista, University of California at Riverside, USA Tanya Berger-Wolf, University of Illinois at Chicago, USA Kanishka Bhaduri, NASA Ames Research Center, USA Mustafa Bilgic, Illinois Institute of Technology, USA Jean-Francois Boulicaut, Universite de Lyon, France Wray Buntine, Canberra Research Laboratory, NICTA, Australia Toon Calders, Eindhoven University of Technology, The Netherlands Michelangelo Ceci, University of Bari "Aldo Moro", Italy Nicolo Cesa-Bianchi, University of Milan, Italy Vineet Chaoji, Yahoo!

Labs, Bangalore, India Nitesh Chawla, University of Notre Dame, USA Ling Chen, University of Technology, Sydney, Australia Hong Cheng, Chinese University of Hong Kong, Hong Kong Diane Cook, Washington State University, USA Alfredo Cuzzocrea, Italian National Research Council, Italy Florence D'Alche-Buc, Universite d'Evry Val d'Essonne, France Kamalika Das, NASA Ames Research Center, USA Christian Desrosiers, Ecole de technologie superieure, Montreal, Canada Chris Ding, University of Texas at Arlington, USA Wei Ding, University of Massachusetts Boston, USA Daniel Dunlavy, Sandia National Laboratories, USA Haimonti Dutta, Columbia University, USA Saso Dzeroski, Jozef Stefan Institute, Slovenia Tina Eliassi-Rad, Rutgers University, USA Ya Ju Fan, Lawrence Livermore National Laboratory, USA Yi Fang, Purdue University, USA Xiaoli Fern, Oregon State University, USA Maurizio Filippone, University of Glasgow, United Kingdom George Forman, Hewlett-Packard Labs, USA Ana Fred, Technical University of Lisbon, Portugal Johannes Furnkranz, Technical University Darmstadt, Germany Joao Gama, University of Porto, Portugal Byron Gao, Texas State University, USA Jing Gao, SUNY Buffalo, USA Claudio Gentile, University of Insubria, Italy Amol Ghoting, IBM T. Watson Research Center, USA Fosca Giannotti ISTI-CNR, Italy Aris Gkoulalas-Divanis, IBM Research, Zurich, Switzerland David Gleich, Purdue University, USA Bart Goethals, University of Antwerp, Belgium Francesco Gullo, Yahoo!

NEM Data Challenge NEM Solutions has recently launched NEM DATA Challenge.

It is a digital challenge in which you can achieve a job inside NEM Solutions’ team, apart from other job offers coming through other partners in this initiative: Siemens-Gamesa Renewable Energy and Tecnalia. Olfactory Cocktail Party Our dataset represents object recognition in the olfactory domain.

It also provides an ideal setting for graduate students and others new to the field to learn about cutting-edge research by hearing outstanding invited speakers and attending presentations and tutorials (included with conference registration).

A set of focused workshops are also held on the last day of the conference.

JRS 2012 Data Mining Competition: Topical Classification of Biomedical Research Papers JRS 2012 Data Mining Competition: Topical Classification of Biomedical Research Papers, is a special event of Joint Rough Sets Symposium (JRS 2012, that will take place in Chengdu, China, August 17-20,...

Materials Identification Based on Measurements of Passively Emitted Electromagnetic Radiation FIND Technologies Inc.

This usually involves using database techniques such as spatial indices.

These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics.


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