Christoph F. Eick
Christoph F. Eick
Associate Professor of Computer Science, University of Houston
Verified email at - Homepage
Cited by
Cited by
Supervised clustering-algorithms and benefits
CF Eick, N Zeidat, Z Zhao
16Th IEEE international conference on tools with artificial intelligence …, 2004
Finding regional co-location patterns for sets of continuous variables in spatial datasets
CF Eick, R Parmar, W Ding, TF Stepinski, JP Nicot
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances …, 2008
Supporting polyploidy in genetic algorithms using dominance vectors
BS Hadad, CF Eick
Evolutionary Programming VI: 6th International Conference, EP97 Indianapolis …, 1997
A framework for regional association rule mining in spatial datasets
W Ding, CF Eick, J Wang, X Yuan
Sixth International Conference on Data Mining (ICDM'06), 851-856, 2006
MOSAIC: A proximity graph approach for agglomerative clustering
J Choo, R Jiamthapthaksin, C Chen, OU Celepcikay, C Giusti, CF Eick
Data Warehousing and Knowledge Discovery: 9th International Conference …, 2007
Using representative-based clustering for nearest neighbor dataset editing
CF Eick, N Zeidat, R Vilalta
Fourth IEEE International Conference on Data Mining (ICDM'04), 375-378, 2004
Visualizing the evolution of genetic algorithm search processes
WB Shine, CF Eick
Proceedings of 1997 IEEE International Conference on Evolutionary …, 1997
A Methodology for the Design and Transformation of Conceptual Schemas.
CF Eick
VLDB 91, 25-34, 1991
Discovery of interesting regions in spatial data sets using supervised clustering
CF Eick, B Vaezian, D Jiang, J Wang
Knowledge Discovery in Databases: PKDD 2006: 10th European Conference on …, 2006
A framework for regional association rule mining and scoping in spatial datasets
W Ding, CF Eick, X Yuan, J Wang, JP Nicot
Geoinformatica 15, 1-28, 2011
Class decomposition via clustering: a new framework for low-variance classifiers
R Vilalta, MK Achari, CF Eick
Third IEEE International Conference on Data Mining, 673-676, 2003
Acquisition of terminological knowledge using database design techniques
CF Eick, PC Lockemann
ACM SIGMOD Record 14 (4), 84-94, 1985
Towards region discovery in spatial datasets
W Ding, R Jiamthapthaksin, R Parmar, D Jiang, TF Stepinski, CF Eick
Advances in Knowledge Discovery and Data Mining: 12th Pacific-Asia …, 2008
Using clustering to learn distance functions for supervised similarity assessment
CF Eick, A Rouhana, A Bagherjeiran, R Vilalta
Engineering Applications of Artificial Intelligence 19 (4), 395-401, 2006
A database clustering methodology and tool
TW Ryu, CF Eick
Information Sciences 171 (1-3), 29-59, 2005
MASSON: discovering commonalities in collection of objects using genetic programming
TW Ryu, CF Eick
Proceedings of the 1st annual conference on genetic programming, 200-208, 1996
Integrating sets, rules, and data in an object-oriented environment
B Czejdo, CF Eick, M Taylor
IEEE Expert 8 (1), 59-66, 1993
Analyzing the composition of cities using spatial clustering
Z Cao, S Wang, G Forestier, A Puissant, CF Eick
Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing, 1-8, 2013
Adaptive clustering: Obtaining better clusters using feedback and past experience
A Bagherjeiran, CF Eick, CS Chen, R Vilalta
Fifth IEEE international conference on data mining (ICDM'05), 4 pp., 2005
From natural language requirements to good data base definitions—a data base design methodology
CF Eick
1984 IEEE First International Conference on Data Engineering, 324-331, 1984
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