Howard J. Hamilton - researchr profile publications

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Publications with a 'Howard J. Hamilton' as author, editor, or advisor:

The TIMERS II Algorithm for the Discovery of CausalityHoward J. Hamilton, Kamran Karimi. pakdd 2005: 744-750 [doi]
Looking Backward, Forward, and All Around: Temporal, Spatial, and Spatio-Temporal Data MiningHoward J. Hamilton, Leah Findlater. flairs 2002: 481-485
Evaluation of Interestingness Measures for Ranking Discovered KnowledgeRobert J. Hilderman, Howard J. Hamilton. pakdd 2001: 247-259 [doi]
Mining functional dependencies from dataHong Yao, Howard J. Hamilton. datamine, 16(2):197-219, 2008. [doi]
FD_Mine: Discovering Functional Dependencies in a Database Using EquivalencesHong Yao, Howard J. Hamilton, Cory J. Butz. icdm 2002: 729-732 [doi]
Variable-sized, circular bokeh depth of field effectsJohannes Moersch, Howard J. Hamilton. graphicsinterface 2014: 103-107 [doi]
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]
Behavior Trees for Modelling Artificial Intelligence in Games: A TutorialRyan Marcotte, Howard J. Hamilton. cgj, 6(3):171-184, 2017. [doi]
Similarity Matching of Temporal Event-Interval SequencesS. Mohammad Mirbagheri, Howard J. Hamilton. ai 2020: 420-425 [doi]
Discovering Temporal/Causal Rules: A Comparison of MethodsKamran Karimi, Howard J. Hamilton. ai 2003: 175-189 [doi]
Finding Temporal Relations: Causal Bayesian Networks vs. C4.5Kamran Karimi, Howard J. Hamilton. ismis 2000: 266-273 [doi]
Distinguishing Causal and Acausal Temporal RelationsKamran Karimi, Howard J. Hamilton. pakdd 2003: 234-240 [doi]
It s About Time: An Introduction to the Special Issue on Temporal Representation and ReasoningScott D. Goodwin, Howard J. Hamilton. ci, 12:357-358, 1996.
Machine Learning of Credible ClassificationsHoward J. Hamilton, Ning Shan, Wojciech Ziarko. ausai 1997: 330-339
Heuristic Selection of Aggregated Temporal Data for Knowledge DiscoveryHoward J. Hamilton, Dee Jay Randall. ieaaie 1999: 714-723
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.
Towards Jointree Propagation with Conditional Probability DistributionsCory J. Butz, Hong Yao, Howard J. Hamilton. rsctc 2004: 368-377 [doi]
Clustering Spatial Data in the Presence of ObstaclesXin Wang, Howard J. Hamilton. flairs 2004: 312-318
Design of knowledge-based systems with the ontology-domain-system approachXin Wang, Christine W. Chan, Howard J. Hamilton. seke 2002: 233-236 [doi]
Learning Complex Concepts Using Crowdsourcing: A Bayesian ApproachPaolo Viappiani, Sandra Zilles, Howard J. Hamilton, Craig Boutilier. aldt 2011: 277-291 [doi]
Effects of Wind on Virtual Plants in AnimationTina L. M. Derzaph, Howard J. Hamilton. ijcgt, 2013, 2013. [doi]
Simulating the Bubble Net Hunting Behaviour of Humpback Whales: The BNH-Whale AlgorithmStacey Hala, Howard J. Hamilton, Paolo Domenici. ai 2016: 40-45 [doi]
Grg: Knowledge Discovery using Information Generalization, Information Reduction, and Rule GenerationNing Shan, Howard J. Hamilton, Nick Cercone. ijait, 5(1-2):99-112, 1996. [doi]
Inducing and Using Decision Rules in the GRG Knowledge Discovery SystemNing Shan, Howard J. Hamilton, Nick Cercone. ecml 1997: 234-241
An ontology-based framework for geospatial clusteringXin Wang, Wei Gu, Danielle Ziébelin, Howard J. Hamilton. gis, 24(11):1601-1630, 2010. [doi]
Retail price time series imputationObaid Ullah Malik, Robert J. Hilderman, Howard J. Hamilton, Richard Dosselmann. ijbidm, 11(1):49-62, 2016. [doi]
Time Prediction of the Next Refueling Event: A Case StudyS. Mohammad Mirbagheri, Howard J. Hamilton. ai 2017: 109-114 [doi]
Word Segmentation Algorithms with Lexical Resources for Hashtag ClassificationCredell Simeon, Howard J. Hamilton, Robert J. Hilderman. dsaa 2016: 743-751 [doi]
Determining the incremental worth of members of an aggregate set through difference-based inductionAvelino J. Gonzalez, Sylvia Daroszewski, Howard J. Hamilton. ijis, 14(3):275-294, 1999. [doi]
Using Rough Sets as Tools for Knowledge DiscoveryNing Shan, Wojciech Ziarko, Howard J. Hamilton, Nick Cercone. kdd 1995: 263-268
Limiting sensitive values in an anonymized table while reducing information loss via p-proportionRichard Dosselmann, Howard J. Hamilton. sap, 5(3), 2022. [doi]
Desirable Elements for a Particle System InterfaceDaniel Schroeder, Howard J. Hamilton. ijcgt, 2014, 2014. [doi]
Discovery of Parameters for Animation of Midge SwarmsJudith Bjorndahl, Ashley Herman, Richard Hamilton, Howard J. Hamilton, Mark Brigham. dis 2015: 17-24 [doi]
Discovering Temporal Rules from Temporally Ordered DataKamran Karimi, Howard J. Hamilton. ideal 2002: 25-30 [doi]
Generalization for Calendar Attributes using Domain Generalization GraphsDee Jay Randall, Howard J. Hamilton, Robert J. Hilderman. time 1998: 177-184 [doi]
Performance evaluation of attribute-oriented algorithms for knowledge discovery from databasesColin L. Carter, Howard J. Hamilton. ictai 1995: 486-489 [doi]
The PDD Framework for Detecting Categories of Peculiar DataMahesh Shrestha, Howard J. Hamilton, Yiyu Yao, Ken Konkel, Liqiang Geng. icdm 2006: 562-571 [doi]
Belief Revision in a Discrete Temporal Probability-LogicScott D. Goodwin, Howard J. Hamilton, Eric Neufeld, Abdul Sattar, André Trudel. time 1994: 113-120 [doi]
PagePrompter: An Intelligent Web Agent Created Using Data Mining TechniquesY. Y. Yao, Howard J. Hamilton, Xuewei Wang. rsctc 2002: 506-513 [doi]
Learning English Grapheme Segmentation Using the Iterated Version Space AlgorithmJianna Jian Zhang, Howard J. Hamilton, Nick Cercone. ismis 1999: 420-429
Efficient spatio-temporal data mining with GenSpace graphsHoward J. Hamilton, Liqiang Geng, Leah Findlater, Dee Jay Randall. japll, 4(2):192-214, 2006. [doi]
Towards an Ontology-Based Spatial Clustering FrameworkXin Wang, Howard J. Hamilton. ai 2005: 205-216 [doi]
Implementation Cost and Efficiency for AI Experience ManagersDavid Thue, Vadim Bulitko, Howard J. Hamilton. aiide 2013: [doi]
Attribute Selection Strategies fro Attribute-Oriented GeneralizationBrock Barber, Howard J. Hamilton. ai 1996: 429-441
Prediction of retail prices of products using local competitorsHassan Waqar Ahmad, Sandra Zilles, Howard J. Hamilton, Richard Dosselmann. ijbidm, 11(1):19-30, 2016. [doi]
Methods for Mining Frequent Sequential PatternsLinhui Jiang, Howard J. Hamilton. ai 2003: 486-491 [doi]
Mining itemset utilities from transaction databasesHong Yao, Howard J. Hamilton. DKE, 59(3):603-626, 2006. [doi]
Logical Decision Rules: Teaching C4.5 to Speak PrologKamran Karimi, Howard J. Hamilton. ideal 2000: 85-90 [doi]
Density-Based Spatial Clustering in the Presence of Obstacles and FacilitatorsXin Wang, Camilo Rostoker, Howard J. Hamilton. pkdd 2004: 446-458 [doi]
Discovering Classification Knowledge in Databases Using Rough SetsNing Shan, Wojciech Ziarko, Howard J. Hamilton, Nick Cercone. kdd 1996: 271-274
Estimating DBLEARN s Potential for Knowledge Discovery in DatabasesHoward J. Hamilton, David R. Fudger. ci, 11:280-296, 1995.
DBRS: A Density-Based Spatial Clustering Method with Random SamplingXin Wang, Howard J. Hamilton. pakdd 2003: 563-575 [doi]
Attribute-oriented Induction Using Domain Generalization GraphsHoward J. Hamilton, Robert J. Hilderman, Nick Cercone. ictai 1996: 246-253
A Bayesian Concept Learning Approach to CrowdsourcingPaolo Viappiani, Sandra Zilles, Howard J. Hamilton, Craig Boutilier. IJCAI 2011: [doi]
CrowdMixer: Multiple Agent Types in Situation-Based Crowd SimulationsShannon Blyth, Howard J. Hamilton. aiide 2006: 15-20
A Foundational Approach to Mining Itemset Utilities from DatabasesHong Yao, Howard J. Hamilton, Cory J. Butz. sdm 2004: 482-486 [doi]
An Adaptive QoS and Trust-Based Lightweight Secure Routing Algorithm for WSNsAditya Pathak, Irfan S. Al-Anbagi, Howard J. Hamilton. iotj, 9(23):23826-23840, 2022. [doi]
Principles for mining summaries using objective measures of interestingnessRobert J. Hilderman, Howard J. Hamilton. ictai 2000: 72-81 [doi]
Parallel Knowledge Discovery Using Domain Generalization GraphsRobert J. Hilderman, Howard J. Hamilton, Robert J. Kowalchuk, Nick Cercone. pkdd 1997: 25-35
Expectation Propagation in GenSpace Graphs for SummarizationLiqiang Geng, Howard J. Hamilton, Larry Korba. dawak 2007: 449-458 [doi]
WebAdaptor: Designing Adaptive Web Sites Using Data Mining TechniquesHoward J. Hamilton, Xuewei Wang, Y. Y. Yao. flairs 2001: 128-132
Using the IIPS Framework to Specify Machine-Discovery ProblemsHoward J. Hamilton, J. Michael Dyck. icci 1992: 266-269
Applying Objective Interestingness Measures in Data Mining SystemsRobert J. Hilderman, Howard J. Hamilton. pkdd 2000: 432-439 [doi]
A Note on Regeneration with Virtual CopiesRobert J. Hilderman, Howard J. Hamilton. TSE, 23(1):56-59, 1997. [doi]
Interestingness measures for data mining: A surveyLiqiang Geng, Howard J. Hamilton. ACM Comput. Surv., 38(3):9, 2006. [doi]
Automatic Generation of Video Game Character Images Using Augmented Structure-and-Style NetworksMatthew T. Mann, Howard J. Hamilton. ai 2019: 387-393 [doi]
Parametric Algorithms for Mining Share-Frequent ItemsetsBrock Barber, Howard J. Hamilton. ismis 2000: 562-572 [doi]
TimeSleuth: A Tool for Discovering Causal and Temporal RulesKamran Karimi, Howard J. Hamilton. ictai 2002: 375-380 [doi]
Mining high utility patterns in interval-based event sequencesS. Mohammad Mirbagheri, Howard J. Hamilton. DKE, 135:101924, 2021. [doi]
Mining Market Basket Data Using Share Measures and Characterized ItemsetsRobert J. Hilderman, Colin L. Carter, Howard J. Hamilton, Nick Cercone. pakdd 1998: 159-170
RFCT: An Association-Based Causality MinerKamran Karimi, Howard J. Hamilton. ai 2002: 334-338 [doi]
GRG: knowledge discovery using information generalization, information reduction, and rule generationNing Shan, Howard J. Hamilton, Nick Cercone. ictai 1995: 372-379 [doi]
ESRS: A Case Selection Algorithm Using Extended Similarity-based Rough SetsLiqiang Geng, Howard J. Hamilton. icdm 2002: 609-612 [doi]
Efficient Representation of Pattern Databases Using Acyclic Random HypergraphsMehdi Sadeqi, Howard J. Hamilton. aips 2016: 258-266 [doi]
Support based measures applied to ice hockey scoring summariesBradley P. Kram, James A. Hall, Howard J. Hamilton. ictai 2000: 352 [doi]
Induction of Classification Rules from Imperfect DataNing Shan, Howard J. Hamilton, Nick Cercone. ismis 1996: 118-127
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]
Proceedings of the Third International Workshop on Temporal Representation and Reasoning, TIME-96, Key West, Florida, USA, May 19-20, 1996Luca Chittaro, Scott D. Goodwin, Howard J. Hamilton, Angelo Montanari, editors, IEEE Computer Society, 1996.
Lossy Compression of Pattern Databases Using Acyclic Random HypergraphsMehdi Sadeqi, Howard J. Hamilton. IJCAI 2017: 4376-4383 [doi]
Parametric Algorithms for Mining Share Frequent ItemsetsBrock Barber, Howard J. Hamilton. jiis, 16(3):277-293, 2001.
Performance Analysis of a Regeneration-Based Dynamic Voting AlgorithmRobert J. Hilderman, Howard J. Hamilton. srds 1995: 196-205
Using Dependence Diagrams to Summarize Decision Rule SetsKamran Karimi, Howard J. Hamilton. ai 2008: 163-172 [doi]
Efficient Removal of Weak Associations in Consensus ClusteringN. C. Ruckiya Sinorina, Howard J. Hamilton, Sandra Zilles. icaart 2022: 326-335 [doi]
Wildfire Occurrence Prediction Using Time Series Classification: A Comparative StudyRyan Laube, Howard J. Hamilton. bigdataconf 2021: 4178-4182 [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]
Learning Pseudo-independent Models: Analytical and Experimental ResultsYang Xiang, Xiaohua Hu, Nick Cercone, Howard J. Hamilton. ai 2000: 227-239 [doi]
Clustering Spatial Data in The Presence of ObstaclesXin Wang, Howard J. Hamilton. ijait, 14(1-2):177-198, 2005. [doi]
Real-Time Validation of Retail Gasoline PricesMondelle Simeon, Howard J. Hamilton. dis 2017: 33-47 [doi]
Learning English Syllabification for WordsJian Zhang, Howard J. Hamilton. ismis 1997: 177-186
Heuristic for Ranking the Interestigness of Discovered KnowledgeRobert J. Hilderman, Howard J. Hamilton. pakdd 1999: 204-209 [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]
Proceedings of the TIME-94 International Workshop on Temporal Reasoning, Pensacola, Florida, USA, May 4, 1994Scott D. Goodwin, Howard J. Hamilton, editors, University of Regina, Regina, SK, Canada, 1994.
An Empirical Comparison of Methods for Iceberg-CUBE ConstructionLeah Findlater, Howard J. Hamilton. flairs 2001: 244-248
Data Mining with Calendar AttributesHoward J. Hamilton, Dee Jay Randall. tsdm 2001: 117-132 [doi]
Early-stage Conflict Detection in HLF-based Delay-critical IoT NetworksAditya Pathak, Irfan S. Al-Anbagi, Howard J. Hamilton. cns 2023: 1-6 [doi]
Iceberg-cube algorithms: An empirical evaluation on synthetic and real dataLeah Findlater, Howard J. Hamilton. ida, 7(2):77-97, 2003. [doi]
Mining Interesting Correlated Contrast SetsMondelle Simeon, Robert J. Hilderman, Howard J. Hamilton. sgai 2012: 49-62 [doi]
Simultaneous Tracking and Activity RecognitionCristina E. Manfredotti, David J. Fleet, Howard J. Hamilton, Sandra Zilles. ictai 2011: 189-196 [doi]
A Technique for Generalizing Temporal Durations in Relational DatabasesDee Jay Randall, Howard J. Hamilton, Robert J. Hilderman. flairs 1998: 193-197
A Comparative Study of Two Density-Based Spatial Clustering Algorithms for Very Large DatasetsXin Wang, Howard J. Hamilton. ai 2005: 120-132 [doi]
Algorithms for Mining Share Frequent Itemsets Containing Infrequent SubsetsBrock Barber, Howard J. Hamilton. pkdd 2000: 316-324 [doi]
Interestingness in Data MiningHoward J. Hamilton. f-egc 2007: 3
FIBS: A Generic Framework for Classifying Interval-Based Temporal SequencesS. Mohammad Mirbagheri, Howard J. Hamilton. dawak 2020: 301-315 [doi]
Trust-based Blockchain Mechanism for V2X Networks in a Smart Grid EnvironmentAditya Pathak, Irfan S. Al-Anbagi, Howard J. Hamilton. ccece 2023: 496-501 [doi]
Transfer Learning and Language Model Adaption for Low Resource Speech RecognitionTasha Baller, Kaelen Bennett, Howard J. Hamilton. ai 2021: [doi]
Measuring the interestingness of discovered knowledge: A principled approachRobert J. Hilderman, Howard J. Hamilton. ida, 7(4):347-382, 2003. [doi]
High-Utility Interval-Based SequencesS. Mohammad Mirbagheri, Howard J. Hamilton. dawak 2020: 107-121 [doi]
Data Mining in Large Databases Using Domain Generalization GraphsRobert J. Hilderman, Howard J. Hamilton, Nick Cercone. jiis, 13(3):195-234, 1999.
Spatio-Temporal Data Mining with Expected Distribution Domain Generalization GraphsHoward J. Hamilton, Liqiang Geng, Leah Findlater, Dee Jay Randall. time 2003: 181-191 [doi]
Efficient Attribute-Oriented Generalization for Knowledge Discovery from Large DatabasesColin L. Carter, Howard J. Hamilton. tkde, 10(2):193-208, 1998. [doi]
Ranking the Interestingness of Summaries from Data Mining SystemsRobert J. Hilderman, Howard J. Hamilton, Brock Barber. flairs 1999: 100-106
A Knowledge Acquisition System for Price Change RulesMoslema Jahan, Howard J. Hamilton. ai 2017: 215-226 [doi]
Real-Time Sentiment-Based Anomaly Detection in Twitter Data StreamsKhantil Patel, Orland Hoeber, Howard J. Hamilton. ai 2015: 196-203 [doi]
Data Visualization in the DB-Discover SystemRobert J. Hilderman, Liangchun Li, Howard J. Hamilton. ictai 1997: 474-477 [doi]
Learning English Syllabification RulesJian Zhang, Howard J. Hamilton. ai 1998: 246-258
A machine-discovery approach to the evaluation of hashing techniquesHoward J. Hamilton, Demyen Doug. jetai, 17(1-2):45-62, 2005. [doi]
Finding Interesting Summaries in GenSpace Graphs EfficientlyLiqiang Geng, Howard J. Hamilton. ai 2004: 89-104 [doi]
Share Based Measures for ItemsetsColin L. Carter, Howard J. Hamilton, Nick Cercone. pkdd 1997: 14-24
A Non-local Coarsening Result in Granular Probabilistic NetworksCory J. Butz, Hong Yao, Howard J. Hamilton. rsfdgrc 2003: 686-689 [doi]
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]
Extracting Share Frequent Itemsets with Infrequent SubsetsBrock Barber, Howard J. Hamilton. datamine, 7(2):153-185, 2003.
A Study of User Interface Modifications in World of WarcraftSean Targett, Victoria Verlysdonk, Howard J. Hamilton, Daryl H. Hepting. gamestudies, 12(2), 2012. [doi]
Heuristic Measures of InterestingnessRobert J. Hilderman, Howard J. Hamilton. pkdd 1999: 232-241
Aggregating Preferences Represented by Conditional Preference NetworksAbu Mohammad Hammad Ali, Howard J. Hamilton, Elizabeth Rayner, Boting Yang, Sandra Zilles. aldt 2021: 3-18 [doi]
Searching for Pattern RulesGuichong Li, Howard J. Hamilton. icdm 2006: 933-937 [doi]
Splitting Large Convolutional Neural Network Layers to Run Real-Time Applications on Mixed-Reality Hardware: Extended AbstractAnthony Beug, Howard J. Hamilton. vr 2022: 700-701 [doi]
Basic Association RulesGuichong Li, Howard J. Hamilton. sdm 2004: 166-177 [doi]
Generalization LatticesHoward J. Hamilton, Robert J. Hilderman, Liangchun Li, Dee Jay Randall. pkdd 1998: 328-336
Causal DiscoveryHong Yao, Cory J. Butz, Howard J. Hamilton. In Oded Maimon, Lior Rokach, editors, The Data Mining and Knowledge Discovery Handbook. pages 945-955, Springer, 2005.
A Bayesian Concept Learning Approach to CrowdsourcingPaolo Viappiani, Sandra Zilles, Howard J. Hamilton, Craig Boutilier. AAAI 2011: [doi]
Learning Models of Activities Involving Interacting ObjectsCristina E. Manfredotti, Kim Steenstrup Pedersen, Howard J. Hamilton, Sandra Zilles. ida 2013: 285-297 [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
A Heuristic for Evaluating Databases for Knowledge Discovery with DBLEARNDavid R. Fudger, Howard J. Hamilton. rsctc 1994: 44-51
TABI: Trust-Based ABAC Mechanism for Edge-IoT Using Blockchain TechnologyAditya Pathak, Irfan S. Al-Anbagi, Howard J. Hamilton. access, 11:36379-36398, 2023. [doi]
Temporal Generalization with Domain Generalization GraphsDee Jay Randall, Howard J. Hamilton, Robert J. Hilderman. ijprai, 13(2):195-217, 1999.
A Comparison of Attribute Selection Strategies for Attribute-Oriented GeneralizationBrock Barber, Howard J. Hamilton. ismis 1997: 106-116
A density-based spatial clustering for physical constraintsXin Wang 0004, Camilo Rostoker, Howard J. Hamilton. jiis, 38(1):269-297, 2012. [doi]

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