O. Celepcikay, and C.F. Eick, A Regional Pattern Discovery Framework using Principal Component Analysis, in Proc. International Conference on Multivariate Statistical Modeling & High Dimensional Data Mining, Kayseri, Turkey, June 2008.

R. Jiamthapthaksin, J. Choo, C. Chen, O. Celepcikay, C. Giusti, and C. F. Eick, MOSAIC: Agglomerative Clustering with Gabriel Graphs, book chapter, in review for publication in October 2008.

C. F. Eick, R. Parmar, W. Ding, T. Stepinki, and J.-P. Nicot, Finding Regional Co-location Patterns for Sets of Continuous Variables in Spatial Datasets, accepted as a full paper for 16th ACM SIGSPATIAL International Conference on Advances in GIS (ACM-GIS), Irvine, California, November 2008.

T. Stepinski, W. Ding, C. F. Eick, Discovering Controlling Factors of Geospatial Variable, accepted as a short paper for 16th ACM SIGSPATIAL International Conference on Advances in GIS (ACM-GIS), Irvine, California, November 2008.

C. Chen, V. Rinsurongkawong, C.F. Eick, M.D. Twa, A Framework for Change Analysis in Spatial Data, in revision.

W. Ding, C. F. Eick, X. Yuan, J. Wang, and J.-P. Nicot, A Framework for Regional Association Rule Mining and Scoping in Spatial Datasets, under review for publication in Geoinformatica.

2007

A. Bagherjeiran and C. F. Eick, Distance Function Learning for Supervised Similarity Assessment, book chapter in P. Perner (eds.): Case-Based Reasoning in Signals and Images, Springer Verlag, August 2007.

R. Miller, L. Miller, and C. F. Eick, Software Tools to Enable Information Accelerated Radical Innovation, in Proc. Portland International Conference on Management of Engineering and Technology (PICMET), Portland, Oregon, August 2007.

J. Choo, R. Jiamthapthaksin, C. Chen, O. Celepcikay, C. Giusti, and C. F. Eick, MOSAIC: A Proximity Graph Approach to Agglomerative Clustering, in Proc. 9th International Conference on Data Warehousing and Knowledge Discovery (DaWaK), acceptance rate: 29%, Regensburg, Germany, September 2007.

W. Ding, T. Stepinski, Dan Jiang, Rachana Parmar and Christoph F Eick, Discovery of Feature-based Hot Spots in Real-valued Databases: an Application to Ground Ice on Mars, submitted to the Journal of Computers and Geoseciences, September 2007.

W. Ding, C. F. Eick, X. Yuan, J. Wang, and J.-P. Nicot, On Regional Association Rule Scoping, in Proc. International Workshop on Spatial and Spatio-Temporal Data Mining (SSTDM), acceptance rate: 29%, Omaha, Nebraska, October 2007.

D. Jiang, C. F. Eick, and C.-S. Chen, On Supervised Density Estimation Techniques and Their Application to Clustering, UH Technical Report UH-CS-07-09, short version appeared in Proc. 15th ACM International Symposium on Advances in Geographic Information Systems (ACM-GIS), Seattle, Washington, November 2007.

Christoph F. Eick, A Unifying Framework for Clustering with Plug-in Fitness Functions and Region Discovery, working paper, December 2007.

2006

N. Zeidat, C. F. Eick, and Z. Zhao, Supervised Clustering: Algorithms and Applications, UH Technical Report UH-CS-06-10, June 2006.

C. F. Eick, A. Rouhana, A. Bagherjeiran, and R. Vilalta, Using Clustering to Learn Distance Functions for Supervised Similarity Assessment, in Engineering Applications of Artificial Intelligence, Volume 19, Issue 4, pp. 395-401, June 2006.

C. F. Eick, B. Vaezian, D. Jiang, and J. Wang, Discovery of Interesting Regions in Spatial Datasets Using Supervised Clustering, in Proc. 10th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD), acceptance rate: 13%, Berlin, Germany, September 2006.

W. Ding, C. F. Eick, J. Wang, and X. Yuan, A Framework for Regional Association Rule Mining in Spatial Datasets, in Proc. IEEE International Conference on Data Mining (ICDM), Acceptance Rate: 19%, Hong Kong, China, December 2006.

J. Choo, R. Jiamthapthaksin, C. Chen, O. Celepcikay, C. Giusti, and C. F. Eick A Hybrid Clustering Technique that Combines Representative-based and Agglomerative Clustering, UH Technical Report UH-CS-06-13, December 2006.

2005

T. Ryu and C. F. Eick, A Database Clustering Methodology and Tool, in Information Sciences 171(1-3): 29-59 (2005).

C. F. Eick and N. Zeidat Using Supervised Clustering to Enhance Classifiers, in Proc. 15th International Symposium on Methodologies for Intelligent Systems (ISMIS), acceptance rate: 36%, Saratoga Springs, New York, pp. 248-256, May 2005.

C. F. Eick, A. Rouhana, A. Bagherjeiran, and R. Vilalta, Using Clustering to Learn Distance Functions for Supervised Similarity Assessment, in Proc. Int. Conf. on Machine Learning and Data Mining (MLDM), acceptance rate: 29%, Leipzig, Germany, pp. 120-131, July 2005.

A. Bagherjeiran, R. Vilalta, and C. F. Eick, Content-Based Image Retrieval Through a Multi-Agent Meta-Learning Framework, International Conference on Tools with AI (ICTAI), acceptance rate: 28%, Hong Kong, China, November 2005.

A. Bagherjeiran, C. F. Eick, C.-S. Chen, and R. Vilalta, Adaptive Clustering: Obtaining Better Clusters Using Feedback and Past Experience, shorter version appeared in Proc. Fifth IEEE International Conference on Data Mining (ICDM), acceptance rate: 21%, Houston, Texas, November 2005.

2004

T. Ryu and C. F. Eick, Systematic Database Summary Generation using the Distributed Query Discovery System, in Proc. Int. Conf of Computational Sciences and its Applications (ICCSA'04), S. Maria degli Angeli, Assisi, Perugia, Italy, pp. 185-195, May 2004.

N. Zeidat and C. F. Eick, K-medoid-style Clustering Algorithms for Supervised Summary Generation, in Proc. 2004 International Conference on Machine Learning; Models, Technologies and Applications (MLMTA'04), acceptance rate: 30%, Las Vegas, Nevada, pp. 932-938, June 2004.

R. Vilalta, M. Achari, and C. F. Eick, Piece-wise Model Fitting using Local Patterns, in Proc. 16th European Conference on Artificial Intelligence (ECAI), acceptance rate: 27%, Valencia, Spain, pp. 559-563, August 2004.

C. F. Eick, N. Zeidat, and R. Vilalta, Using Representative-Based Clustering for Nearest Neighbor Dataset Editing, in Proc. Fourth IEEE International Conference on Data Mining (ICDM), acceptance rate: 22%, Brighton, England, pp. 375-378, November 2004.

C. F. Eick, N. Zeidat, and Z. Zhao, Supervised Clustering --- Algorithms and Benefits, short version appeared in Proc. International Conference on Tools with AI (ICTAI), acceptance rate: 30%, Boca Raton, Florida, pp. 774-776, November 2004.

W. Chen, C. F. Eick, and J.-F. Paris, A Two-Expert Approach to File Access Prediction, in Proc. 3rd International Information and Telecommunication Technologies Symposium (I2TS), Sao Carlos, Brazil, December 2004.

2003

X. Li and C. F. Eick, Fast Decision Tree Learning Algorithms for Microarray Data Collections, in Proc. Int. Conference on Machine Learning and Application (ICMLA), acceptance rate: 32%, Los Angeles, California, pp. 37-43 June 2003.

R. Vilalta, M. Achari, and C. F. Eick, Class Decomposition via Clustering: A New Framework for Low-Variance Classifiers, in Proc. Third IEEE International Conference on Data Mining (ICDM), acceptance rate: 24%, Melbourne, Florida, pp. 673-676, November 2003.

Remark: conference acceptence rates are reported based on the category the paper is accepted in; for example, if a conference accepted 12% of the submitted papers as long papers and another 11% as poster papers, then if our paper was accepted as a long paper we will report an accepteance rate of 12%, whereas if the paper was just accepted as a poster paper, an acceptance rate of 23% is reported.

last updated: August 24, 2008