Interestingness measures for data mining: A survey L Geng, HJ Hamilton ACM Computing Surveys (CSUR) 38 (3), 9, 2006 | 1175 | 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 | 415 | 2004 |

Mining itemset utilities from transaction databases H Yao, HJ Hamilton Data & Knowledge Engineering 59 (3), 603-626, 2006 | 308 | 2006 |

Knowledge discovery and measures of interest RJ Hilderman, HJ Hamilton Springer Science & Business Media, 2013 | 241 | 2013 |

Knowledge discovery and interestingness measures: A survey RJ Hilderman, HJ Hamilton Department of Computer Science, University of Regina, 1999 | 201 | 1999 |

Quality measures in data mining F Guillet, HJ Hamilton Springer, 2007 | 158 | 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 | 145 | 2006 |

Efficient attribute-oriented generalization for knowledge discovery from large databases CL Carter, HJ Hamilton IEEE Transactions on Knowledge & Data Engineering, 193-208, 1998 | 124 | 1998 |

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 | 121 | 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 |

Extracting share frequent itemsets with infrequent subsets B Barber, HJ Hamilton Data Mining and Knowledge Discovery 7 (2), 153-185, 2003 | 103 | 2003 |

Mining functional dependencies from data H Yao, HJ Hamilton Data Mining and Knowledge Discovery 16 (2), 197-219, 2008 | 102 | 2008 |

RIAC: a rule induction algorithm based on approximate classification HJ Hamilton, N Cercone, N Shan Computer Science Department, University of Regina, 1996 | 100 | 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 | 73 | 1997 |

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 |

FD/spl I. bar/Mine: discovering functional dependencies in a database using equivalences H Yao, HJ Hamilton, CJ Butz Data Mining, 2002. ICDM 2003. Proceedings. 2002 IEEE International …, 2002 | 61 | 2002 |

Heuristic measures of interestingness RJ Hilderman, HJ Hamilton European Conference on Principles of Data Mining and Knowledge Discovery …, 1999 | 61 | 1999 |

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 | 60 | 2004 |

Applying objective interestingness measures in data mining systems RJ Hilderman, HJ Hamilton European Conference on Principles of Data Mining and Knowledge Discovery …, 2000 | 60 | 2000 |