V. Rinsurongkawong and C.F. Eick, Correspondence Clustering: An Approach to Cluster Multiple Related Spatial Datasets, to appear in Proc. Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), acceptance rate: 10%, Hyderabad, India, June 2010.
R. Jiamthapthaksin, C. F. Eick, and S. Lee, GAC-GEO: A Generic Agglomerative Clustering Framework for Geo-referenced Datasets, under review for a journal, January 2010.
Ricardo Vilalta, Luis Real, Christoph F. Eick, and Vasile Palade, A Bridge between Traditional Supervised Learning and Pattern Theory, submitted to a conference, January 2010.
Chun-Sheng Chen, Vadeerat Rinsurongkawong, Anurag Nagar, and Christoph F. Eick, Mining Trajectories using Non-Parametic Density Functions, submitted to a conference, February 2010.
2009
W. Ding, T. Stepinski, D. Jiang, R. Parmar and C. F. Eick,
Discovery of Feature-based Hot Spots Using Supervised Clustering,
in International Journal of Computers &
Geosciences, Elsevier, March 2009.
R. Jiamthapthaksin, C. F. Eick, and V. Rinsurongkawong, An Architecture and Algorithms for Multi-Run Clustering, in Proc. Computational Intelligence Symposium on Data Mining (CIDM), Nashville, Tennessee, April 2009.
R. Jiamthapthaksin, J. Choo, C.-S. Chen, O. Celepcikay, C. Giusti, and C. F. Eick, MOSAIC: Agglomerative Clustering with Gabriel Graphs, book chapter in Tho Man Nguyen (Eds), Complex Data Warehousing and Knowledge Discovery for Advanced Retrieval Development: Innovative Methods and Applications, IGI Publishing, Spring 2009.
Chun-Sheng Chen, Vadeerat Rinsurongkawong, Christoph F. Eick, Michael D. Twa, Change Analysis in Spatial Data by Combining Contouring Algorithms with Supervised Density Functions in Proc. PAKDD, Bangkok 2009: 907-914.
A. Bagherjeiran, O. U. Celepcikay, R. Jiamthapthaksin, C.-S. Chen, V. Rinsurongkawong, S. Lee, J. Thomas, and C. F. Eick, Cougar^2: An Open Source Machine Learning and Data Mining Development Framework, in Proc. Open Source Data Mining Workshop (OSDM), Bangkok, Thailand, April 2009.
J. Thomas, and C. F. Eick, Online Learning of Spacecraft Simulation Models, acceptance rate 31%, in Proc. of the 21st Innovative Applications of Artificial Intelligence Conference (IAAI), Pasadena, California, July 2009.
O. U. Celepcikay, C. F. Eick, and C. Ordonez, Regional Pattern Discovery in Geo-referenced Datasets Using PCA, in Proc. Fifth Int. Conf. on Machine Learning and Data Mining (MLDM), acceptance rate: 30%, Leipzig, Germany, July 2009.
C. F. Eick, O. U. Celepcikay, R. Jiamthapthaksin, and V. Rinsurongkawong, A Unifying Domain-driven Framework for Clustering with Plug-in Fitness Functions and Region Discovery, submitted for publication to a journal.
R. Jiamthapthaksin, C. F. Eick, and R. Vilalta, A Framework for Multi-Objective Clustering and its Application to Co-Location Mining, in Proc. Fifth International Conference on Advanced Data Mining and Applications (ADMA), acceptance rate: 12%, Beijing, China, August 2009.
O.U. Celepcikay and C. F. Eick, REG^2: A Regional Regression Framework for Geo-Referenced Datasets, to appear in Proc. 17th ACM SIGSPATIAL International Conference on Advances in GIS (ACM-GIS), acceptance rate: 20%, Seattle, Washington, November 2009.
V. Rinsurongkawong, C.-S. Chen, C.F. Eick, and M.D. Twa, Analyzing Change in Spatial Data by Utilizing Polygon Models, in revision.
2008
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, submitted to Geoinformatica.
W. Ding, R. Jiamthapthaksin, R. Parmar, D. Jiang, T. Stepinski, and C. F. Eick, Towards Region Discovery in Spatial Datasets, in Proc. Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), acceptance rate: 12%, Osaka, Japan, May 2008.
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, 2008.
O. U. 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.
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, in Proc. 16th ACM SIGSPATIAL International Conference on Advances in GIS (ACM-GIS), acceptance rate: 19%, Irvine, California, November 2008.
T. Stepinski, W. Ding, and C. F. Eick, Discovering Controlling Factors of Geospatial Variables, in Proc. 16th ACM SIGSPATIAL International Conference on Advances in GIS (ACM-GIS), acceptance rate: 37%, Irvine, California, November 2008.
V. Rinsurongkawong, and C. F. Eick, Change Analysis in Spatial Datasets by Interestingness Comparison, accepted as a ACM-GIS Conference PhD Showcase Paper, acceptance rate: 42%, in ACM-SIGSPATIAL Newsletter, Decemember 2008.
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.-S. 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, 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.-S. 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.