| 2008 |
|---|
| 90 | EE | Kamran Karimi,
Howard J. Hamilton:
Using Dependence Diagrams to Summarize Decision Rule Sets.
Canadian Conference on AI 2008: 163-172 |
| 89 | EE | Hong Yao,
Howard J. Hamilton:
Mining functional dependencies from data.
Data Min. Knowl. Discov. 16(2): 197-219 (2008) |
| 2007 |
|---|
| 88 | | Fabrice Guillet,
Howard J. Hamilton:
Quality Measures in Data Mining
Springer 2007 |
| 87 | EE | Liqiang Geng,
Howard J. Hamilton,
Larry Korba:
Expectation Propagation in GenSpace Graphs for Summarization.
DaWaK 2007: 449-458 |
| 86 | | Howard J. Hamilton:
Interestingness in Data Mining.
EGC 2007: 3 |
| 85 | EE | Liqiang Geng,
Howard J. Hamilton:
Choosing the Right Lens: Finding What is Interesting in Data Mining.
Quality Measures in Data Mining 2007: 3-24 |
| 2006 |
|---|
| 84 | | Shannon Blyth,
Howard J. Hamilton:
CrowdMixer: Multiple Agent Types in Situation-Based Crowd Simulations.
AIIDE 2006: 15-20 |
| 83 | EE | Mahesh Shrestha,
Howard J. Hamilton,
Yiyu Yao,
Ken Konkel,
Liqiang Geng:
The PDD Framework for Detecting Categories of Peculiar Data.
ICDM 2006: 562-571 |
| 82 | EE | Guichong Li,
Howard J. Hamilton:
Searching for Pattern Rules.
ICDM 2006: 933-937 |
| 81 | EE | Liqiang Geng,
Howard J. Hamilton:
Interestingness measures for data mining: A survey.
ACM Comput. Surv. 38(3): (2006) |
| 80 | EE | Hong Yao,
Howard J. Hamilton:
Mining itemset utilities from transaction databases.
Data Knowl. Eng. 59(3): 603-626 (2006) |
| 79 | EE | Howard J. Hamilton,
Liqiang Geng,
Leah Findlater,
Dee Jay Randall:
Efficient spatio-temporal data mining with GenSpace graphs.
J. Applied Logic 4(2): 192-214 (2006) |
| 2005 |
|---|
| 78 | EE | Xin Wang,
Howard J. Hamilton:
A Comparative Study of Two Density-Based Spatial Clustering Algorithms for Very Large Datasets.
Canadian Conference on AI 2005: 120-132 |
| 77 | EE | Xin Wang,
Howard J. Hamilton:
Towards an Ontology-Based Spatial Clustering Framework.
Canadian Conference on AI 2005: 205-216 |
| 76 | EE | Howard J. Hamilton,
Kamran Karimi:
The TIMERS II Algorithm for the Discovery of Causality.
PAKDD 2005: 744-750 |
| 75 | | Hong Yao,
Cory J. Butz,
Howard J. Hamilton:
Causal Discovery.
The Data Mining and Knowledge Discovery Handbook 2005: 945-955 |
| 74 | EE | Xin Wang,
Howard J. Hamilton:
Clustering Spatial Data in The Presence of Obstacles.
International Journal on Artificial Intelligence Tools 14(1-2): 177-198 (2005) |
| 73 | EE | Howard J. Hamilton,
Demyen Doug:
A machine-discovery approach to the evaluation of hashing techniques.
J. Exp. Theor. Artif. Intell. 17(1-2): 45-62 (2005) |
| 2004 |
|---|
| 72 | EE | Liqiang Geng,
Howard J. Hamilton:
Finding Interesting Summaries in GenSpace Graphs Efficiently.
Canadian Conference on AI 2004: 89-104 |
| 71 | | Xin Wang,
Howard J. Hamilton:
Clustering Spatial Data in the Presence of Obstacles.
FLAIRS Conference 2004 |
| 70 | EE | Xin Wang,
Camilo Rostoker,
Howard J. Hamilton:
Density-Based Spatial Clustering in the Presence of Obstacles and Facilitators.
PKDD 2004: 446-458 |
| 69 | EE | Cory J. Butz,
Hong Yao,
Howard J. Hamilton:
Towards Jointree Propagation with Conditional Probability Distributions.
Rough Sets and Current Trends in Computing 2004: 368-377 |
| 68 | EE | Hong Yao,
Howard J. Hamilton,
Cory J. Butz:
A Foundational Approach to Mining Itemset Utilities from Databases.
SDM 2004 |
| 67 | EE | Guichong Li,
Howard J. Hamilton:
Basic Association Rules.
SDM 2004 |
| 2003 |
|---|
| 66 | EE | Kamran Karimi,
Howard J. Hamilton:
Discovering Temporal/Causal Rules: A Comparison of Methods.
Canadian Conference on AI 2003: 175-189 |
| 65 | EE | Linhui Jiang,
Howard J. Hamilton:
Methods for Mining Frequent Sequential Patterns.
Canadian Conference on AI 2003: 486-491 |
| 64 | EE | Kamran Karimi,
Howard J. Hamilton:
Distinguishing Causal and Acausal Temporal Relations.
PAKDD 2003: 234-240 |
| 63 | EE | Xin Wang,
Howard J. Hamilton:
DBRS: A Density-Based Spatial Clustering Method with Random Sampling.
PAKDD 2003: 563-575 |
| 62 | EE | Cory J. Butz,
Hong Yao,
Howard J. Hamilton:
A Non-local Coarsening Result in Granular Probabilistic Networks.
RSFDGrC 2003: 686-689 |
| 61 | EE | Howard J. Hamilton,
Liqiang Geng,
Leah Findlater,
Dee Jay Randall:
Spatio-Temporal Data Mining with Expected Distribution Domain Generalization Graphs.
TIME 2003: 181-191 |
| 60 | | Brock Barber,
Howard J. Hamilton:
Extracting Share Frequent Itemsets with Infrequent Subsets.
Data Min. Knowl. Discov. 7(2): 153-185 (2003) |
| 59 | EE | Leah Findlater,
Howard J. Hamilton:
Iceberg-cube algorithms: An empirical evaluation on synthetic and real data.
Intell. Data Anal. 7(2): 77-97 (2003) |
| 58 | EE | Robert J. Hilderman,
Howard J. Hamilton:
Measuring the interestingness of discovered knowledge: A principled approach.
Intell. Data Anal. 7(4): 347-382 (2003) |
| 2002 |
|---|
| 57 | EE | Kamran Karimi,
Howard J. Hamilton:
RFCT: An Association-Based Causality Miner.
Canadian Conference on AI 2002: 334-338 |
| 56 | | Howard J. Hamilton,
Leah Findlater:
Looking Backward, Forward, and All Around: Temporal, Spatial, and Spatio-Temporal Data Mining.
FLAIRS Conference 2002: 481-485 |
| 55 | EE | Liqiang Geng,
Howard J. Hamilton:
ESRS: A Case Selection Algorithm Using Extended Similarity-based Rough Sets.
ICDM 2002: 609-612 |
| 54 | EE | Hong Yao,
Howard J. Hamilton,
Cory J. Butz:
FD_Mine: Discovering Functional Dependencies in a Database Using Equivalences.
ICDM 2002: 729-732 |
| 53 | EE | Kamran Karimi,
Howard J. Hamilton:
TimeSleuth: A Tool for Discovering Causal and Temporal Rules.
ICTAI 2002: 375-380 |
| 52 | EE | Kamran Karimi,
Howard J. Hamilton:
Discovering Temporal Rules from Temporally Ordered Data.
IDEAL 2002: 25-30 |
| 51 | EE | 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 |
| 50 | EE | Xin Wang,
Christine W. Chan,
Howard J. Hamilton:
Design of knowledge-based systems with the ontology-domain-system approach.
SEKE 2002: 233-236 |
| 2001 |
|---|
| 49 | | Howard J. Hamilton,
Xuewei Wang,
Y. Y. Yao:
WebAdaptor: Designing Adaptive Web Sites Using Data Mining Techniques.
FLAIRS Conference 2001: 128-132 |
| 48 | | Leah Findlater,
Howard J. Hamilton:
An Empirical Comparison of Methods for Iceberg-CUBE Construction.
FLAIRS Conference 2001: 244-248 |
| 47 | EE | Robert J. Hilderman,
Howard J. Hamilton:
Evaluation of Interestingness Measures for Ranking Discovered Knowledge.
PAKDD 2001: 247-259 |
| 46 | | Brock Barber,
Howard J. Hamilton:
Parametric Algorithms for Mining Share Frequent Itemsets.
J. Intell. Inf. Syst. 16(3): 277-293 (2001) |
| 2000 |
|---|
| 45 | | 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 |
| 44 | EE | Yang Xiang,
Xiaohua Hu,
Nick Cercone,
Howard J. Hamilton:
Learning Pseudo-independent Models: Analytical and Experimental Results.
Canadian Conference on AI 2000: 227-239 |
| 43 | EE | Bradley P. Kram,
James A. Hall,
Howard J. Hamilton:
Support based measures applied to ice hockey scoring summaries.
ICTAI 2000: 352- |
| 42 | EE | Robert J. Hilderman,
Howard J. Hamilton:
Principles for mining summaries using objective measures of interestingness.
ICTAI 2000: 72-81 |
| 41 | EE | Kamran Karimi,
Howard J. Hamilton:
Logical Decision Rules: Teaching C4.5 to Speak Prolog.
IDEAL 2000: 85-90 |
| 40 | EE | Kamran Karimi,
Howard J. Hamilton:
Finding Temporal Relations: Causal Bayesian Networks vs. C4.5.
ISMIS 2000: 266-273 |
| 39 | EE | Brock Barber,
Howard J. Hamilton:
Parametric Algorithms for Mining Share-Frequent Itemsets.
ISMIS 2000: 562-572 |
| 38 | EE | Brock Barber,
Howard J. Hamilton:
Algorithms for Mining Share Frequent Itemsets Containing Infrequent Subsets.
PKDD 2000: 316-324 |
| 37 | EE | Robert J. Hilderman,
Howard J. Hamilton:
Applying Objective Interestingness Measures in Data Mining Systems.
PKDD 2000: 432-439 |
| 36 | EE | 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 |
| 35 | EE | Howard J. Hamilton,
Dee Jay Randall:
Data Mining with Calendar Attributes.
TSDM 2000: 117-132 |
| 1999 |
|---|
| 34 | | Robert J. Hilderman,
Howard J. Hamilton,
Brock Barber:
Ranking the Interestingness of Summaries from Data Mining Systems.
FLAIRS Conference 1999: 100-106 |
| 33 | | Howard J. Hamilton,
Dee Jay Randall:
Heuristic Selection of Aggregated Temporal Data for Knowledge Discovery.
IEA/AIE 1999: 714-723 |
| 32 | | Jianna Jian Zhang,
Howard J. Hamilton,
Nick Cercone:
Learning English Grapheme Segmentation Using the Iterated Version Space Algorithm.
ISMIS 1999: 420-429 |
| 31 | EE | Robert J. Hilderman,
Howard J. Hamilton:
Heuristic for Ranking the Interestigness of Discovered Knowledge.
PAKDD 1999: 204-209 |
| 30 | | Robert J. Hilderman,
Howard J. Hamilton:
Heuristic Measures of Interestingness.
PKDD 1999: 232-241 |
| 29 | | Dee Jay Randall,
Howard J. Hamilton,
Robert J. Hilderman:
Temporal Generalization with Domain Generalization Graphs.
IJPRAI 13(2): 195-217 (1999) |
| 28 | | 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) |
| 1998 |
|---|
| 27 | | Jian Zhang,
Howard J. Hamilton:
Learning English Syllabification Rules.
Canadian Conference on AI 1998: 246-258 |
| 26 | | Dee Jay Randall,
Howard J. Hamilton,
Robert J. Hilderman:
A Technique for Generalizing Temporal Durations in Relational Databases.
FLAIRS Conference 1998: 193-197 |
| 25 | | 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 |
| 24 | | 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 |
| 23 | | Howard J. Hamilton,
Robert J. Hilderman,
Liangchun Li,
Dee Jay Randall:
Generalization Lattices.
PKDD 1998: 328-336 |
| 22 | EE | Dee Jay Randall,
Howard J. Hamilton,
Robert J. Hilderman:
Generalization for Calendar Attributes using Domain Generalization Graphs.
TIME 1998: 177-184 |
| 21 | EE | 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) |
| 20 | EE | 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) |
| 1997 |
|---|
| 19 | | Howard J. Hamilton,
Ning Shan,
Wojciech Ziarko:
Machine Learning of Credible Classifications.
Australian Joint Conference on Artificial Intelligence 1997: 330-339 |
| 18 | | Ning Shan,
Howard J. Hamilton,
Nick Cercone:
Inducing and Using Decision Rules in the GRG Knowledge Discovery System.
ECML 1997: 234-241 |
| 17 | EE | Robert J. Hilderman,
Liangchun Li,
Howard J. Hamilton:
Data Visualization in the DB-Discover System.
ICTAI 1997: 474-477 |
| 16 | | Brock Barber,
Howard J. Hamilton:
A Comparison of Attribute Selection Strategies for Attribute-Oriented Generalization.
ISMIS 1997: 106-116 |
| 15 | | Jian Zhang,
Howard J. Hamilton:
Learning English Syllabification for Words.
ISMIS 1997: 177-186 |
| 14 | | Colin L. Carter,
Howard J. Hamilton,
Nick Cercone:
Share Based Measures for Itemsets.
PKDD 1997: 14-24 |
| 13 | | Robert J. Hilderman,
Howard J. Hamilton,
Robert J. Kowalchuk,
Nick Cercone:
Parallel Knowledge Discovery Using Domain Generalization Graphs.
PKDD 1997: 25-35 |
| 12 | EE | Robert J. Hilderman,
Howard J. Hamilton:
A Note on Regeneration with Virtual Copies.
IEEE Trans. Software Eng. 23(1): 56-59 (1997) |
| 1996 |
|---|
| 11 | | Brock Barber,
Howard J. Hamilton:
Attribute Selection Strategies fro Attribute-Oriented Generalization.
Canadian Conference on AI 1996: 429-441 |
| 10 | | Howard J. Hamilton,
Robert J. Hilderman,
Nick Cercone:
Attribute-oriented Induction Using Domain Generalization Graphs.
ICTAI 1996: 246-253 |
| 9 | | Ning Shan,
Howard J. Hamilton,
Nick Cercone:
Induction of Classification Rules from Imperfect Data.
ISMIS 1996: 118-127 |
| 8 | | Ning Shan,
Wojciech Ziarko,
Howard J. Hamilton,
Nick Cercone:
Discovering Classification Knowledge in Databases Using Rough Sets.
KDD 1996: 271-274 |
| 7 | | 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) |
| 1995 |
|---|
| 6 | | Ning Shan,
Wojciech Ziarko,
Howard J. Hamilton,
Nick Cercone:
Using Rough Sets as Tools for Knowledge Discovery.
KDD 1995: 263-268 |
| 5 | | Robert J. Hilderman,
Howard J. Hamilton:
Performance Analysis of a Regeneration-Based Dynamic Voting Algorithm.
SRDS 1995: 196-205 |
| 4 | | Howard J. Hamilton,
David R. Fudger:
Estimating DBLEARN's Potential for Knowledge Discovery in Databases.
Computational Intelligence 11: 280-296 (1995) |
| 1994 |
|---|
| 3 | EE | Scott D. Goodwin,
Howard J. Hamilton,
Eric Neufeld,
Abdul Sattar,
André Trudel:
Belief Revision in a Discrete Temporal Probability-Logic.
TIME 1994: 113-120 |
| 1993 |
|---|
| 2 | | David R. Fudger,
Howard J. Hamilton:
A Heuristic for Evaluating Databases for Knowledge Discovery with DBLEARN.
RSKD 1993: 44-51 |
| 1992 |
|---|
| 1 | | Howard J. Hamilton,
J. Michael Dyck:
Using the IIPS Framework to Specify Machine-Discovery Problems.
ICCI 1992: 266-269 |
