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Note: Links lead to the DBLP on the Web. Howard J. Hamilton Liqiang Geng , Howard J. Hamilton: Finding Interesting Summaries in GenSpace Graphs Efficiently. Canadian Conference on AI 2004 : 89-104 Kamran Karimi , Howard J. Hamilton: Discovering Temporal/Causal Rules: A Comparison of Methods. Canadian Conference on AI 2003 : 175-189 Linhui Jiang , Howard J. Hamilton: Methods for Mining Frequent Sequential Patterns. Canadian Conference on AI 2003 : 486-491 Kamran Karimi , Howard J. Hamilton: Distinguishing Causal and Acausal Temporal Relations. PAKDD 2003 : 234-240 Xin Wang , Howard J. Hamilton: DBRS: A Density-Based Spatial Clustering Method with Random Sampling. PAKDD 2003 : 563-575 Cory J. Butz , Hong Yao , Howard J. Hamilton: A Non-local Coarsening Result in Granular Probabilistic Networks. RSFDGrC 2003 : 686-689 Howard J. Hamilton, Liqiang Geng , Leah Findlater , Dee Jay Randall : Spatio-Temporal Data Mining with Expected Distribution Domain Generalization Graphs. TIME 2003 : 181-191 Brock Barber , Howard J. Hamilton: Extracting Share Frequent Itemsets with Infrequent Subsets. Data Min. Knowl. Discov. 7 (2): 153-185 (2003) Leah Findlater , Howard J. Hamilton: Iceberg-cube algorithms: An empirical evaluation on synthetic and real data. Intell. Data Anal. 7 (2): 77-97 (2003) Robert J. Hilderman , Howard J. Hamilton: Measuring the interestingness of discovered knowledge: A principled approach. Intell. Data Anal. 7 (4): 347-382 (2003) Kamran Karimi , Howard J. Hamilton: RFCT: An Association-Based Causality Miner. Canadian Conference on AI 2002 : 334-338 Howard J. Hamilton, Leah Findlater : Looking Backward, Forward, and All Around: Temporal, Spatial, and Spatio-Temporal Data Mining. FLAIRS Conference 2002 : 481-485 Liqiang Geng , Howard J. Hamilton: ESRS: A Case Selection Algorithm Using Extended Similarity-based Rough Sets. ICDM 2002 : 609-612 Hong Yao , Howard J. Hamilton, Cory J. Butz : FD_Mine: Discovering Functional Dependencies in a Database Using Equivalences. ICDM 2002 : 729-732 Kamran Karimi , Howard J. Hamilton: TimeSleuth: A Tool for Discovering Causal and Temporal Rules. ICTAI 2002 : 375-380 Kamran Karimi , Howard J. Hamilton: Discovering Temporal Rules from Temporally Ordered Data. IDEAL 2002 : 25-30 Y. Y. Yao , Howard J. Hamilton, Xuewei Wang : PagePrompter: An Intelligent Web Agent Created Using Data Mining Techniques. Rough Sets and Current Trends in Computing 2002 : 506-513 Xin Wang , Christine W. Chan , Howard J. Hamilton: Design of knowledge-based systems with the ontology-domain-system approach. SEKE 2002 : 233-236 Howard J. Hamilton, Xuewei Wang , Y. Y. Yao : WebAdaptor: Designing Adaptive Web Sites Using Data Mining Techniques. FLAIRS Conference 2001 : 128-132 Leah Findlater , Howard J. Hamilton: An Empirical Comparison of Methods for Iceberg-CUBE Construction. FLAIRS Conference 2001 : 244-248 Robert J. Hilderman , Howard J. Hamilton: Evaluation of Interestingness Measures for Ranking Discovered Knowledge. PAKDD 2001 : 247-259 Brock Barber , Howard J. Hamilton: Parametric Algorithms for Mining Share Frequent Itemsets. J. Intell. Inf. Syst. 16 (3): 277-293 (2001) Howard J. Hamilton: Advances in Artificial Intelligence, 13th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI 2000, Montréal, Quebec, Canada, May 14-17, 2000, Proceedings Springer 2000 Yang Xiang , Xiaohua Hu , Nick Cercone , Howard J. Hamilton: Learning Pseudo-independent Models: Analytical and Experimental Results. Canadian Conference on AI 2000 : 227-239 Bradley P. Kram , James A. Hall , Howard J. Hamilton: Support based measures applied to ice hockey scoring summaries. ICTAI 2000 : 352- Robert J. Hilderman , Howard J. Hamilton: Principles for mining summaries using objective measures of interestingness. ICTAI 2000 : 72-81 Kamran Karimi , Howard J. Hamilton: Logical Decision Rules: Teaching C4.5 to Speak Prolog. IDEAL 2000 : 85-90 Kamran Karimi , Howard J. Hamilton: Finding Temporal Relations: Causal Bayesian Networks vs. C4.5. ISMIS 2000 : 266-273 Brock Barber , Howard J. Hamilton: Parametric Algorithms for Mining Share-Frequent Itemsets. ISMIS 2000 : 562-572 Brock Barber , Howard J. Hamilton: Algorithms for Mining Share Frequent Itemsets Containing Infrequent Subsets. PKDD 2000 : 316-324 Robert J. Hilderman , Howard J. Hamilton: Applying Objective Interestingness Measures in Data Mining Systems. PKDD 2000 : 432-439 Kamran Karimi , Julia A. Johnson , Howard J. Hamilton: A Proposal for Including Behavior in the Process of Object Similarity Assessment with Examples from Artificial Life. Rough Sets and Current Trends in Computing 2000 : 642-646 Howard J. Hamilton, Dee Jay Randall : Data Mining with Calendar Attributes. TSDM 2000 : 117-132 Robert J. Hilderman , Howard J. Hamilton, Brock Barber : Ranking the Interestingness of Summaries from Data Mining Systems. FLAIRS Conference 1999 : 100-106 Howard J. Hamilton, Dee Jay Randall : Heuristic Selection of Aggregated Temporal Data for Knowledge Discovery. IEA/AIE 1999 : 714-723 Jianna Jian Zhang , Howard J. Hamilton, Nick Cercone : Learning English Grapheme Segmentation Using the Iterated Version Space Algorithm. ISMIS 1999 : 420-429 Robert J. Hilderman , Howard J. Hamilton: Heuristic for Ranking the Interestigness of Discovered Knowledge. PAKDD 1999 : 204-209 Robert J. Hilderman , Howard J. Hamilton: Heuristic Measures of Interestingness. PKDD 1999 : 232-241 Dee Jay Randall , Howard J. Hamilton, Robert J. Hilderman : Temporal Generalization with Domain Generalization Graphs. IJPRAI 13 (2): 195-217 (1999) Robert J. Hilderman , Howard J. Hamilton, Nick Cercone : Data Mining in Large Databases Using Domain Generalization Graphs. J. Intell. Inf. Syst. 13 (3): 195-234 (1999) Jian Zhang , Howard J. Hamilton: Learning English Syllabification Rules. Canadian Conference on AI 1998 : 246-258 Dee Jay Randall , Howard J. Hamilton, Robert J. Hilderman : A Technique for Generalizing Temporal Durations in Relational Databases. FLAIRS Conference 1998 : 193-197 Avelino J. Gonzalez , Sylvia Daroszewski , Howard J. Hamilton: Determining the Incremental Worth of Members of an Aggregate Set through Difference-Based Induction. FLAIRS Conference 1998 : 245-249 Robert J. Hilderman , Colin L. Carter , Howard J. Hamilton, Nick Cercone : Mining Market Basket Data Using Share Measures and Characterized Itemsets. PAKDD 1998 : 159-170 Howard J. Hamilton, Robert J. Hilderman , Liangchun Li , Dee Jay Randall : Generalization Lattices. PKDD 1998 : 328-336 Dee Jay Randall , Howard J. Hamilton, Robert J. Hilderman : Generalization for Calendar Attributes using Domain Generalization Graphs. TIME 1998 : 177-184 Colin L. Carter , Howard J. Hamilton: Efficient Attribute-Oriented Generalization for Knowledge Discovery from Large Databases. IEEE Trans. Knowl. Data Eng. 10 (2): 193-208 (1998) Robert J. Hilderman , Howard J. Hamilton, Colin L. Carter , Nick Cercone : Mining Association Rules from Market Basket Data using Share Measures and Characterized Itemsets. International Journal on Artificial Intelligence Tools 7 (2): 189-220 (1998) Howard J. Hamilton, Ning Shan , Wojciech Ziarko : Machine Learning of Credible Classifications. Australian Joint Conference on Artificial Intelligence 1997 : 330-339 Ning Shan , Howard J. Hamilton, Nick Cercone : Inducing and Using Decision Rules in the GRG Knowledge Discovery System. ECML 1997 : 234-241 Robert J. Hilderman , Liangchun Li , Howard J. Hamilton: Data Visualization in the DB-Discover System. ICTAI 1997 : 474-477 Brock Barber , Howard J. Hamilton: A Comparison of Attribute Selection Strategies for Attribute-Oriented Generalization. ISMIS 1997 : 106-116 Jian Zhang , Howard J. Hamilton: Learning English Syllabification for Words. ISMIS 1997 : 177-186 Colin L. Carter , Howard J. Hamilton, Nick Cercone : Share Based Measures for Itemsets. PKDD 1997 : 14-24 Robert J. Hilderman , Howard J. Hamilton, Robert J. Kowalchuk , Nick Cercone : Parallel Knowledge Discovery Using Domain Generalization Graphs. PKDD 1997 : 25-35 Robert J. Hilderman , Howard J. Hamilton: A Note on Regeneration with Virtual Copies. IEEE Trans. Software Eng. 23 (1): 56-59 (1997) Brock Barber , Howard J. Hamilton: Attribute Selection Strategies fro Attribute-Oriented Generalization. Canadian Conference on AI 1996 : 429-441 Howard J. Hamilton, Robert J. Hilderman , Nick Cercone : Attribute-oriented Induction Using Domain Generalization Graphs. ICTAI 1996 : 246-253 Ning Shan , Howard J. Hamilton, Nick Cercone : Induction of Classification Rules from Imperfect Data. ISMIS 1996 : 118-127 Ning Shan , Wojciech Ziarko , Howard J. Hamilton, Nick Cercone : Discovering Classification Knowledge in Databases Using Rough Sets. KDD 1996 : 271-274 Scott D. Goodwin , Howard J. Hamilton: It's About Time: An Introduction to the Special Issue on Temporal Representation and Reasoning. Computational Intelligence 12 : 357-358 (1996) Ning Shan , Wojciech Ziarko , Howard J. Hamilton, Nick Cercone : Using Rough Sets as Tools for Knowledge Discovery. KDD 1995 : 263-268 Robert J. Hilderman , Howard J. Hamilton: Performance Analysis of a Regeneration-Based Dynamic Voting Algorithm. Symposium on Reliable Distributed Systems 1995 : 196-205 Howard J. Hamilton, David R. Fudger : Estimating DBLEARN's Potential for Knowledge Discovery in Databases. Computational Intelligence 11 : 280-296 (1995) Scott D. Goodwin , Howard J. Hamilton, Eric Neufeld , Abdul Sattar , André Trudel : Belief Revision in a Discrete Temporal Probability-Logic. TIME 1994 : 113-120 David R. Fudger , Howard J. Hamilton: A Heuristic for Evaluating Databases for Knowledge Discovery with DBLEARN. RSKD 1993 : 44-51 Howard J. Hamilton, J. Michael Dyck : Using the IIPS Framework to Specify Machine-Discovery Problems. ICCI 1992 : 266-269 1 [ 11 ] [ 16 ] [ 34 ] [ 38 ] [ 39 ] [ 46 ] [ 60 ] 2 [ 54 ] [ 62 ] 3 [ 14 ] [ 20 ] [ 21 ] [ 24 ] 4 [ 6 ] [ 8 ] [ 9 ] [ 10 ] [ 13 ] [ 14 ] [ 18 ] [ 20 ] [ 24 ] [ 28 ] [ 32 ] [ 44 ] 5 [ 50 ] 6 [ 25 ] 7 [ 1 ] 8 [ 48 ] [ 56 ] [ 59 ] [ 61 ] 9 [ 2 ] [ 4 ] 10 [ 55 ] [ 61 ] [ 67 ] 11 [ 25 ] 12 [ 3 ] [ 7 ] 13 [ 43 ] 14 [ 5 ] [ 10 ] [ 12 ] [ 13 ] [ 17 ] [ 20 ] [ 22 ] [ 23 ] [ 24 ] [ 26 ] [ 28 ] [ 29 ] [ 30 ] [ 31 ] [ 34 ] [ 37 ] [ 42 ] [ 47 ] [ 58 ] 15 [ 44 ] 16 [ 65 ] 17 [ 36 ] 18 [ 36 ] [ 40 ] [ 41 ] [ 52 ] [ 53 ] [ 57 ] [ 64 ] [ 66 ] 19 [ 13 ] 20 [ 43 ] 21 [ 17 ] [ 23 ] 22 [ 3 ] 23 [ 22 ] [ 23 ] [ 26 ] [ 29 ] [ 33 ] [ 35 ] [ 61 ] 24 [ 3 ] 25 [ 6 ] [ 8 ] [ 9 ] [ 18 ] [ 19 ] 26 [ 3 ] 27 [ 50 ] [ 63 ] 28 [ 49 ] [ 51 ] 29 [ 44 ] 30 [ 54 ] [ 62 ] 31 [ 49 ] [ 51 ] 32 [ 15 ] [ 27 ] 33 [ 32 ] 34 [ 6 ] [ 8 ] [ 19 ]  ©2004 Association for Computing Machinery |
