Christoph F. Eick's Recent Publications and Submissions

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.

2013

S. Wang, T. Cai and C.F. Eick, New Spatiotemporal Clustering Algorithms and their Applications to Ozone Pollution, to appear 8th International Workshop on Spatial and Spatio-Temporal Data Mining (SSTDM), Dallas, Texas, December 2013.

C. F. Eick, F. Akdag, P. Amalaman, A. Tadakaluru, A Framework for Discriminative Polygonal Place Scoping, to appear ACM SIGSPATIAL Workshop on Computational Models of Place, Orlando, Florida, November 2013.

S. Wang and C. F. Eick, A Polygon-based Clustering and Analysis Framework for Mining Spatial Datasets, to appear in Geoinformatica.

F. Akdag, C. F. Eick, and G. Chen, Creating Polygon Models for Spatial Clusters, in revision, July 2013

Z. Cao, S. Wang, G. Forestier, A. Puissant, and C. F. Eick, Analyzing the Composition of Cities Using Spatial Clustering, in Proc. 2nd ACM SIGKDD International Workshop on Urban Computing (UrbComp'13), acceptance rate: 24%, Chicago, Illinois, August 2013.

P. K. Amalaman, C. F. Eick, and N. Rizk, Using Turning Point Dection to Obtain Better Regression Trees, in Proc. Int. Conference on Machine Learning and Data Mining (MLDM), New York City, New York, July 2013.

C. F. Eick, Uncertainty Management for Fuzzy Decision Support Systems, CoRR abs/1304.2351, 2013.


2012

R. Xu, S. Chandrasekaran, B. Chapman, C. F. Eick, Directive-based Programming Models for Scientific Applications --- A Comparison, in Proc. Second International Workshop on Domain-Specific Languages and High-Level Frameworks for High Performance Computing (WOLFHPC), collocated with Supercomputing (SC), Salt Lake City, Utah, November 2012.

C.-S. Chen, N. Shaikh, P. Charoenrattanaruk, C.F. Eick, N. Rizk, and E. Gabriel, Design and Evaluation of a Parallel Execution Framework for the CLEVER Clustering Algorithm, presented at Parallel Computing Conference 2011 (ParCo), acceptance rate: 31%, Ghent, Belgium, September 2011; published in K. De Bosschere et al. (Eds.), Application and Tools and Techniques on the Road to Exascale Computing, IOS Press, pp. 73-80, May 2012.

W. Ding, and C. F. Eick, Regional Association Rule Mining and Scoping from Spatial Data, Data Mining: Foundations and Intelligent Paradigms, pp. 289-313, March 2012.


2011

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, Geoinformatica (2011) 15:1-28, DOI 10.1007/s10707-010-0111-6, January 2011.

R. Miller, C.-S. Chen, C. F. Eick, Abraham Bagherjeiran, A Framework for Spatial Feature Selction and Scoping and its Application to Geo-Targeting, in Proc. First IEEE Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM), Fuzhou, China, July 2011.

R. Jiamthapthaksin, C. F. Eick, and S. Lee, GAC-GEO: A Generic Agglomerative Clustering Framework for Geo-referenced Datasets, in Knowledge and Information Systems (KAIS).

C.-S. Chen, C. F. Eick, and N. Rizk, Mining Spatial Trajectories using Non-Parametric Density Functions, in Proc. Int. Conference on Machine Learning and Data Mining (MLDM), New York City, New York, September 2011.


2010

T. Stepinski, W. Ding, and C. F. Eick, Controlling Patterns of Geospatial Phenomena, Geoinformatica.

V. Rinsurongkawong and C. F. Eick, Correspondence Clustering: An Approach to Cluster Multiple Related Spatial Datasets, in Proc. Asia-Pacific Conference on Knowledge Discovery and Data Mining (PAKDD), acceptance rate: 10%, Hyderabad, India, June 2010.

V. Rinsurongkawong, C-S. Chen, C. F. Eick, and M. Twa, Analyzing Change in Spatial Data by Utilizing Polygon Models, in Proc. International Conference on Computing for Geospatial Research & Application, Washington DC, June 2010.

Sujing Wang, Chun-Sheng Chen, Vadeerat Rinsurongkawong, Fatih Akdag, and C. F. Eick, A Polygon-based Methodology for Mining Related Spatial Datasets, to appear in Proc. ACM SIGSPATIAL International Workshop on Data Mining for Geoinformatics (DMG), San Jose, California, November 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, acceptance rate: 29%, in Proc. PAKDD p. 907-914, Bangkok Thailand, May 2009.

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, in Proc. 17th ACM SIGSPATIAL International Conference on Advances in GIS (ACM-GIS), acceptance rate: 20%, Seattle, Washington, November 2009.


2008

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, Decemeber 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.


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last updated: September 30, 2013