| Interestingness measures for data mining: A survey L Geng, HJ Hamilton ACM Computing Surveys (CSUR) 38 (3), 9, 2006 | 1172 | 2006 |
| A foundational approach to mining itemset utilities from databases H Yao, HJ Hamilton, CJ Butz Proceedings of the 2004 SIAM International Conference on Data Mining, 482-486, 2004 | 409 | 2004 |
| Mining itemset utilities from transaction databases H Yao, HJ Hamilton Data & Knowledge Engineering 59 (3), 603-626, 2006 | 312 | 2006 |
| Knowledge discovery and measures of interest RJ Hilderman, HJ Hamilton Springer Science & Business Media, 2013 | 237 | 2013 |
| Knowledge discovery and interestingness measures: A survey RJ Hilderman, HJ Hamilton Department of Computer Science, University of Regina, 1999 | 202 | 1999 |
| Quality measures in data mining F Guillet, HJ Hamilton Springer, 2007 | 182 | 2007 |
| A unified framework for utility-based measures for mining itemsets H Yao, HJ Hamilton, L Geng Proc. of ACM SIGKDD 2nd Workshop on Utility-Based Data Mining, 28-37, 2006 | 141 | 2006 |
| DBRS: a density-based spatial clustering method with random sampling X Wang, HJ Hamilton Pacific-Asia Conference on Knowledge Discovery and Data Mining, 563-575, 2003 | 125 | 2003 |
| Evaluation of interestingness measures for ranking discovered knowledge RJ Hilderman, HJ Hamilton Pacific-Asia Conference on Knowledge Discovery and Data Mining, 247-259, 2001 | 120 | 2001 |
| Efficient attribute-oriented generalization for knowledge discovery from large databases CL Carter, HJ Hamilton IEEE Transactions on knowledge and data engineering 10 (2), 193-208, 1998 | 116 | 1998 |
| Extracting share frequent itemsets with infrequent subsets B Barber, HJ Hamilton Data Mining and Knowledge Discovery 7 (2), 153-185, 2003 | 105 | 2003 |
| Mining functional dependencies from data H Yao, HJ Hamilton Data Mining and Knowledge Discovery 16 (2), 197-219, 2008 | 103 | 2008 |
| RIAC: a rule induction algorithm based on approximate classification HJ Hamilton, N Cercone, N Shan Computer Science Department, University of Regina, 1996 | 99 | 1996 |
| Using Rough Sets as Tools for Knowledge Discovery. N Shan, W Ziarko, HJ Hamilton, N Cercone KDD, 263-268, 1995 | 78 | 1995 |
| Share based measures for itemsets CL Carter, HJ Hamilton, N Cercone European Symposium on Principles of Data Mining and Knowledge Discovery, 14-24, 1997 | 71 | 1997 |
| FD/spl I. bar/Mine: discovering functional dependencies in a database using equivalences H Yao, HJ Hamilton, CJ Butz 2002 IEEE International Conference on Data Mining, 2002. Proceedings., 729-732, 2002 | 66 | 2002 |
| Choosing the right lens: Finding what is interesting in data mining L Geng, HJ Hamilton Quality measures in data mining, 3-24, 2007 | 65 | 2007 |
| Density-based spatial clustering in the presence of obstacles and facilitators X Wang, C Rostoker, HJ Hamilton European Conference on Principles of Data Mining and Knowledge Discovery …, 2004 | 61 | 2004 |
| Applying objective interestingness measures in data mining systems RJ Hilderman, HJ Hamilton European Conference on Principles of Data Mining and Knowledge Discovery …, 2000 | 61 | 2000 |
| Heuristic measures of interestingness RJ Hilderman, HJ Hamilton European Conference on Principles of Data Mining and Knowledge Discovery …, 1999 | 61 | 1999 |
