Howard J. Hamilton - researchr alias

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2010

An ontology-based framework for geospatial clusteringXin Wang, Wei Gu, Danielle Ziébelin, Howard J. Hamilton. gis, 24(11):1601-1630, 2010. [doi]

2009

Visualizing Privacy Implications of Access Control Policies in Social Network SystemsMohd M. Anwar, Philip W. L. Fong, Xue Dong Yang, Howard J. Hamilton. esorics 2010: 106-120 [doi]

The Multi-Tree Cubing algorithm for computing iceberg cubesXing Li, Howard J. Hamilton, Kamran Karimi, Liqiang Geng. jiis, 33(2):179-208, 2009. [doi]

2008

Using Dependence Diagrams to Summarize Decision Rule SetsKamran Karimi, Howard J. Hamilton. ai 2008: 163-172 [doi]

Mining functional dependencies from dataHong Yao, Howard J. Hamilton. datamine, 16(2):197-219, 2008. [doi]

2007

Choosing the Right Lens: Finding What is Interesting in Data MiningLiqiang Geng, Howard J. Hamilton. In Fabrice Guillet, Howard J. Hamilton, editors, Quality Measures in Data Mining. Volume 43 of Studies in Computational Intelligence, pages 3-24, Springer, 2007. [doi]

Expectation Propagation in GenSpace Graphs for SummarizationLiqiang Geng, Howard J. Hamilton, Larry Korba. dawak 2007: 449-458 [doi]

Quality Measures in Data MiningFabrice Guillet, Howard J. Hamilton, editors, Volume 43 of Studies in Computational Intelligence, Springer, 2007. [doi]

Interestingness in Data MiningHoward J. Hamilton. f-egc 2007: 3

2006

2005

2004

2003

Extracting Share Frequent Itemsets with Infrequent SubsetsBrock Barber, Howard J. Hamilton. datamine, 7(2):153-185, 2003.

A Non-local Coarsening Result in Granular Probabilistic NetworksCory J. Butz, Hong Yao, Howard J. Hamilton. rsfdgrc 2003: 686-689 [doi]

Iceberg-cube algorithms: An empirical evaluation on synthetic and real dataLeah Findlater, Howard J. Hamilton. ida, 7(2):77-97, 2003. [doi]

Spatio-Temporal Data Mining with Expected Distribution Domain Generalization GraphsHoward J. Hamilton, Liqiang Geng, Leah Findlater, Dee Jay Randall. time 2003: 181-191 [doi]

Measuring the interestingness of discovered knowledge: A principled approachRobert J. Hilderman, Howard J. Hamilton. ida, 7(4):347-382, 2003. [doi]

Methods for Mining Frequent Sequential PatternsLinhui Jiang, Howard J. Hamilton. ai 2003: 486-491 [doi]

Distinguishing Causal and Acausal Temporal RelationsKamran Karimi, Howard J. Hamilton. pakdd 2003: 234-240 [doi]

Discovering Temporal/Causal Rules: A Comparison of MethodsKamran Karimi, Howard J. Hamilton. ai 2003: 175-189 [doi]

DBRS: A Density-Based Spatial Clustering Method with Random SamplingXin Wang, Howard J. Hamilton. pakdd 2003: 563-575 [doi]

2002

ESRS: A Case Selection Algorithm Using Extended Similarity-based Rough SetsLiqiang Geng, Howard J. Hamilton. icdm 2002: 609-612 [doi]

Looking Backward, Forward, and All Around: Temporal, Spatial, and Spatio-Temporal Data MiningHoward J. Hamilton, Leah Findlater. flairs 2002: 481-485

TimeSleuth: A Tool for Discovering Causal and Temporal RulesKamran Karimi, Howard J. Hamilton. ictai 2002: 375-380 [doi]

RFCT: An Association-Based Causality MinerKamran Karimi, Howard J. Hamilton. ai 2002: 334-338 [doi]

Discovering Temporal Rules from Temporally Ordered DataKamran Karimi, Howard J. Hamilton. ideal 2002: 25-30 [doi]

Design of knowledge-based systems with the ontology-domain-system approachXin Wang, Christine W. Chan, Howard J. Hamilton. seke 2002: 233-236 [doi]

FD_Mine: Discovering Functional Dependencies in a Database Using EquivalencesHong Yao, Howard J. Hamilton, Cory J. Butz. icdm 2002: 729-732 [doi]

PagePrompter: An Intelligent Web Agent Created Using Data Mining TechniquesY. Y. Yao, Howard J. Hamilton, Xuewei Wang. rsctc 2002: 506-513 [doi]

2001

Parametric Algorithms for Mining Share Frequent ItemsetsBrock Barber, Howard J. Hamilton. jiis, 16(3):277-293, 2001.

An Empirical Comparison of Methods for Iceberg-CUBE ConstructionLeah Findlater, Howard J. Hamilton. flairs 2001: 244-248

WebAdaptor: Designing Adaptive Web Sites Using Data Mining TechniquesHoward J. Hamilton, Xuewei Wang, Y. Y. Yao. flairs 2001: 128-132

Evaluation of Interestingness Measures for Ranking Discovered KnowledgeRobert J. Hilderman, Howard J. Hamilton. pakdd 2001: 247-259 [doi]

2000

Parametric Algorithms for Mining Share-Frequent ItemsetsBrock Barber, Howard J. Hamilton. ismis 2000: 562-572 [doi]

Algorithms for Mining Share Frequent Itemsets Containing Infrequent SubsetsBrock Barber, Howard J. Hamilton. pkdd 2000: 316-324 [doi]

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, ProceedingsHoward J. Hamilton, editor, Volume 1822 of Lecture Notes in Computer Science, Springer, 2000.

Data Mining with Calendar AttributesHoward J. Hamilton, Dee Jay Randall. tsdm 2001: 117-132 [doi]

Principles for mining summaries using objective measures of interestingnessRobert J. Hilderman, Howard J. Hamilton. ictai 2000: 72-81 [doi]

Applying Objective Interestingness Measures in Data Mining SystemsRobert J. Hilderman, Howard J. Hamilton. pkdd 2000: 432-439 [doi]

Finding Temporal Relations: Causal Bayesian Networks vs. C4.5Kamran Karimi, Howard J. Hamilton. ismis 2000: 266-273 [doi]

A Proposal for Including Behavior in the Process of Object Similarity Assessment with Examples from Artificial LifeKamran Karimi, Julia A. Johnson, Howard J. Hamilton. rsctc 2001: 642-646 [doi]

Logical Decision Rules: Teaching C4.5 to Speak PrologKamran Karimi, Howard J. Hamilton. ideal 2000: 85-90 [doi]

Support based measures applied to ice hockey scoring summariesBradley P. Kram, James A. Hall, Howard J. Hamilton. ictai 2000: 352 [doi]

Learning Pseudo-independent Models: Analytical and Experimental ResultsYang Xiang, Xiaohua Hu, Nick Cercone, Howard J. Hamilton. ai 2000: 227-239 [doi]

1999

Heuristic Selection of Aggregated Temporal Data for Knowledge DiscoveryHoward J. Hamilton, Dee Jay Randall. ieaaie 1999: 714-723

Heuristic for Ranking the Interestigness of Discovered KnowledgeRobert J. Hilderman, Howard J. Hamilton. pakdd 1999: 204-209 [doi]

Heuristic Measures of InterestingnessRobert J. Hilderman, Howard J. Hamilton. pkdd 1999: 232-241

Ranking the Interestingness of Summaries from Data Mining SystemsRobert J. Hilderman, Howard J. Hamilton, Brock Barber. flairs 1999: 100-106

Data Mining in Large Databases Using Domain Generalization GraphsRobert J. Hilderman, Howard J. Hamilton, Nick Cercone. jiis, 13(3):195-234, 1999.

Temporal Generalization with Domain Generalization GraphsDee Jay Randall, Howard J. Hamilton, Robert J. Hilderman. ijprai, 13(2):195-217, 1999.

Learning English Grapheme Segmentation Using the Iterated Version Space AlgorithmJianna Jian Zhang, Howard J. Hamilton, Nick Cercone. ismis 1999: 420-429

1998

Efficient Attribute-Oriented Generalization for Knowledge Discovery from Large DatabasesColin L. Carter, Howard J. Hamilton. tkde, 10(2):193-208, 1998. [doi]

Determining the Incremental Worth of Members of an Aggregate Set through Difference-Based InductionAvelino J. Gonzalez, Sylvia Daroszewski, Howard J. Hamilton. flairs 1998: 245-249

Generalization LatticesHoward J. Hamilton, Robert J. Hilderman, Liangchun Li, Dee Jay Randall. pkdd 1998: 328-336

Mining Market Basket Data Using Share Measures and Characterized ItemsetsRobert J. Hilderman, Colin L. Carter, Howard J. Hamilton, Nick Cercone. pakdd 1998: 159-170

Mining Association Rules from Market Basket Data using Share Measures and Characterized ItemsetsRobert J. Hilderman, Howard J. Hamilton, Colin L. Carter, Nick Cercone. ijait, 7(2):189-220, 1998. [doi]

A Technique for Generalizing Temporal Durations in Relational DatabasesDee Jay Randall, Howard J. Hamilton, Robert J. Hilderman. flairs 1998: 193-197

Generalization for Calendar Attributes using Domain Generalization GraphsDee Jay Randall, Howard J. Hamilton, Robert J. Hilderman. time 1998: 177-184 [doi]

Learning English Syllabification RulesJian Zhang, Howard J. Hamilton. ai 1998: 246-258

1997

A Comparison of Attribute Selection Strategies for Attribute-Oriented GeneralizationBrock Barber, Howard J. Hamilton. ismis 1997: 106-116

Share Based Measures for ItemsetsColin L. Carter, Howard J. Hamilton, Nick Cercone. pkdd 1997: 14-24

Machine Learning of Credible ClassificationsHoward J. Hamilton, Ning Shan, Wojciech Ziarko. ausai 1997: 330-339

A Note on Regeneration with Virtual CopiesRobert J. Hilderman, Howard J. Hamilton. TSE, 23(1):56-59, 1997. [doi]

Data Visualization in the DB-Discover SystemRobert J. Hilderman, Liangchun Li, Howard J. Hamilton. ictai 1997: 474-477 [doi]

Parallel Knowledge Discovery Using Domain Generalization GraphsRobert J. Hilderman, Howard J. Hamilton, Robert J. Kowalchuk, Nick Cercone. pkdd 1997: 25-35

Inducing and Using Decision Rules in the GRG Knowledge Discovery SystemNing Shan, Howard J. Hamilton, Nick Cercone. ecml 1997: 234-241

Learning English Syllabification for WordsJian Zhang, Howard J. Hamilton. ismis 1997: 177-186

1996

1995

Estimating DBLEARN s Potential for Knowledge Discovery in DatabasesHoward J. Hamilton, David R. Fudger. ci, 11:280-296, 1995.

Performance Analysis of a Regeneration-Based Dynamic Voting AlgorithmRobert J. Hilderman, Howard J. Hamilton. srds 1995: 196-205

Using Rough Sets as Tools for Knowledge DiscoveryNing Shan, Wojciech Ziarko, Howard J. Hamilton, Nick Cercone. kdd 1995: 263-268

1994

Belief Revision in a Discrete Temporal Probability-LogicScott D. Goodwin, Howard J. Hamilton, Eric Neufeld, Abdul Sattar, André Trudel. time 1994: 113-120 [doi]

1993

A Heuristic for Evaluating Databases for Knowledge Discovery with DBLEARNDavid R. Fudger, Howard J. Hamilton. rsctc 1994: 44-51

1992

Using the IIPS Framework to Specify Machine-Discovery ProblemsHoward J. Hamilton, J. Michael Dyck. icci 1992: 266-269