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ORCIDHoward J. Hamilton
Howard John Hamilton
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- affiliation: University of Regina, Canada
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2020 – today
- 2024
[j34]Aditya Pathak
, Irfan S. Al-Anbagi, Howard J. Hamilton:
Blockchain-Enhanced Zero Knowledge Proof-Based Privacy-Preserving Mutual Authentication for IoT Networks. IEEE Access 12: 118618-118636 (2024)- [j33]Aditya Pathak, Irfan S. Al-Anbagi, Howard J. Hamilton:
SATI: Sidechain-Based Access Control & Trust Mechanism for IoT Networks. IEEE Trans. Netw. Serv. Manag. 21(5): 5888-5903 (2024) - [c102]Aditya Pathak, Irfan S. Al-Anbagi, Howard J. Hamilton:
Privacy-Preserving Authentication Mechanism for P2P Energy Trading in Smart Grid Networks. ICC 2024: 3085-3090 - 2023
- [j32]Aditya Pathak, Irfan S. Al-Anbagi, Howard J. Hamilton:
TABI: Trust-Based ABAC Mechanism for Edge-IoT Using Blockchain Technology. IEEE Access 11: 36379-36398 (2023) - [c101]Aditya Pathak, Irfan S. Al-Anbagi, Howard J. Hamilton:
Trust-based Blockchain Mechanism for V2X Networks in a Smart Grid Environment. CCECE 2023: 496-501 - [c100]Aditya Pathak, Irfan S. Al-Anbagi, Howard J. Hamilton:
Early-stage Conflict Detection in HLF-based Delay-critical IoT Networks. CNS 2023: 1-6 - 2022
- [j31]Aditya Pathak, Irfan S. Al-Anbagi, Howard J. Hamilton:
An Adaptive QoS and Trust-Based Lightweight Secure Routing Algorithm for WSNs. IEEE Internet Things J. 9(23): 23826-23840 (2022) - [j30]Richard Dosselmann, Howard J. Hamilton:
Limiting sensitive values in an anonymized table while reducing information loss via p-proportion. Secur. Priv. 5(3) (2022) - [c99]N. C. Ruckiya Sinorina, Howard J. Hamilton, Sandra Zilles:
Efficient Removal of Weak Associations in Consensus Clustering. ICAART (3) 2022: 326-335 - [c98]Anthony Beug, Howard J. Hamilton:
Splitting Large Convolutional Neural Network Layers to Run Real-Time Applications on Mixed-Reality Hardware: Extended Abstract. VR Workshops 2022: 700-701 - 2021
- [j29]S. Mohammad Mirbagheri, Howard J. Hamilton:
Mining high utility patterns in interval-based event sequences. Data Knowl. Eng. 135: 101924 (2021) - [c97]Tasha Baller, Kaelen Bennett, Howard J. Hamilton:
Transfer Learning and Language Model Adaption for Low Resource Speech Recognition. Canadian AI 2021 - [c96]Abu Mohammad Hammad Ali, Howard J. Hamilton, Elizabeth Rayner, Boting Yang, Sandra Zilles:
Aggregating Preferences Represented by Conditional Preference Networks. ADT 2021: 3-18 - [c95]Ryan Laube, Howard J. Hamilton:
Wildfire Occurrence Prediction Using Time Series Classification: A Comparative Study. IEEE BigData 2021: 4178-4182 - [c94]Matthew Allan Hamilton, Anthony Paul Beug, Howard John Hamilton, Wil James Norton:
Augmented Reality Technology for People Living with Dementia and their Care Partners. ICVARS 2021: 21-30 - 2020
- [c93]S. Mohammad Mirbagheri, Howard J. Hamilton:
Similarity Matching of Temporal Event-Interval Sequences. Canadian AI 2020: 420-425 - [c92]S. Mohammad Mirbagheri, Howard J. Hamilton:
High-Utility Interval-Based Sequences. DaWaK 2020: 107-121 - [c91]S. Mohammad Mirbagheri, Howard J. Hamilton:
FIBS: A Generic Framework for Classifying Interval-Based Temporal Sequences. DaWaK 2020: 301-315
2010 – 2019
- 2019
- [c90]Matthew T. Mann, Howard J. Hamilton:
Automatic Generation of Video Game Character Images Using Augmented Structure-and-Style Networks. Canadian AI 2019: 387-393 - [i4]Richard Dosselmann, Mehdi Sadeqi, Howard J. Hamilton:
A Tutorial on Computing t-Closeness. CoRR abs/1911.11212 (2019) - [i3]S. Mohammad Mirbagheri, Howard J. Hamilton:
FIBS: A Generic Framework for Classifying Interval-based Temporal Sequences. CoRR abs/1912.09445 (2019) - [i2]S. Mohammad Mirbagheri, Howard J. Hamilton:
High Utility Interval-Based Sequences. CoRR abs/1912.11165 (2019) - 2017
- [j28]Ryan Marcotte, Howard J. Hamilton:
Behavior Trees for Modelling Artificial Intelligence in Games: A Tutorial. Comput. Games J. 6(3): 171-184 (2017) - [c89]S. Mohammad Mirbagheri, Howard J. Hamilton:
Time Prediction of the Next Refueling Event: A Case Study. Canadian AI 2017: 109-114 - [c88]Moslema Jahan, Howard J. Hamilton:
A Knowledge Acquisition System for Price Change Rules. Canadian AI 2017: 215-226 - [c87]Mondelle Simeon, Howard J. Hamilton:
Real-Time Validation of Retail Gasoline Prices. DS 2017: 33-47 - [c86]Mehdi Sadeqi, Howard J. Hamilton:
Lossy Compression of Pattern Databases Using Acyclic Random Hypergraphs. IJCAI 2017: 4376-4383 - 2016
- [j27]Hassan Waqar Ahmad, Sandra Zilles, Howard J. Hamilton, Richard Dosselmann:
Prediction of retail prices of products using local competitors. Int. J. Bus. Intell. Data Min. 11(1): 19-30 (2016) - [j26]Obaid Ullah Malik, Robert J. Hilderman, Howard J. Hamilton, Richard Dosselmann:
Retail price time series imputation. Int. J. Bus. Intell. Data Min. 11(1): 49-62 (2016) - [c85]Stacey Hala, Howard J. Hamilton, Paolo Domenici:
Simulating the Bubble Net Hunting Behaviour of Humpback Whales: The BNH-Whale Algorithm. Canadian AI 2016: 40-45 - [c84]Mehdi Sadeqi, Howard J. Hamilton:
Efficient Representation of Pattern Databases Using Acyclic Random Hypergraphs. ICAPS 2016: 258-266 - [c83]Credell Simeon, Howard J. Hamilton, Robert J. Hilderman:
Word Segmentation Algorithms with Lexical Resources for Hashtag Classification. DSAA 2016: 743-751 - 2015
- [c82]Khantil Patel, Orland Hoeber, Howard J. Hamilton:
Real-Time Sentiment-Based Anomaly Detection in Twitter Data Streams. Canadian AI 2015: 196-203 - [c81]Judith Bjorndahl, Ashley Herman, Richard Hamilton, Howard J. Hamilton, Mark Brigham:
Discovery of Parameters for Animation of Midge Swarms. Discovery Science 2015: 17-24 - 2014
- [j25]Daniel Schroeder, Howard J. Hamilton:
Desirable Elements for a Particle System Interface. Int. J. Comput. Games Technol. 2014: 623809:1-623809:12 (2014) - [c80]Johannes Moersch, Howard J. Hamilton:
Variable-sized, circular bokeh depth of field effects. Graphics Interface 2014: 103-107 - 2013
- [j24]Tina L. M. Derzaph, Howard J. Hamilton:
Effects of Wind on Virtual Plants in Animation. Int. J. Comput. Games Technol. 2013: 674848:1-674848:11 (2013) - [c79]David Thue, Vadim Bulitko, Howard J. Hamilton:
Implementation Cost and Efficiency for AI Experience Managers. Intelligent Narrative Technologies 2013 - [c78]Cristina E. Manfredotti, Kim Steenstrup Pedersen, Howard J. Hamilton, Sandra Zilles:
Learning Models of Activities Involving Interacting Objects. IDA 2013: 285-297 - 2012
- [j23]Sean Targett, Victoria Verlysdonk, Howard J. Hamilton, Daryl H. Hepting:
A Study of User Interface Modifications in World of Warcraft. Game Stud. 12(2) (2012) - [j22]Xin Wang, Camilo Rostoker, Howard J. Hamilton:
A density-based spatial clustering for physical constraints. J. Intell. Inf. Syst. 38(1): 269-297 (2012) - [c77]Mondelle Simeon, Robert J. Hilderman, Howard J. Hamilton:
Mining Interesting Correlated Contrast Sets. SGAI Conf. 2012: 49-62 - 2011
- [c76]Paolo Viappiani, Sandra Zilles, Howard J. Hamilton, Craig Boutilier:
A Bayesian Concept Learning Approach to Crowdsourcing. Interactive Decision Theory and Game Theory 2011 - [c75]Paolo Viappiani, Sandra Zilles, Howard J. Hamilton, Craig Boutilier:
Learning Complex Concepts Using Crowdsourcing: A Bayesian Approach. ADT 2011: 277-291 - [c74]Cristina E. Manfredotti, David J. Fleet, Howard J. Hamilton, Sandra Zilles:
Simultaneous Tracking and Activity Recognition. ICTAI 2011: 189-196 - [c73]Paolo Viappiani, Sandra Zilles, Howard J. Hamilton, Craig Boutilier:
A Bayesian Concept Learning Approach to Crowdsourcing. ITWP@IJCAI 2011 - 2010
- [j21]Xin Wang, Wei Gu, Danielle Ziébelin, Howard J. Hamilton:
An ontology-based framework for geospatial clustering. Int. J. Geogr. Inf. Sci. 24(11): 1601-1630 (2010) - [p3]Hong Yao, Cory J. Butz, Howard J. Hamilton:
Causal Discovery. Data Mining and Knowledge Discovery Handbook 2010: 949-958 - [i1]Kamran Karimi, Howard J. Hamilton:
Generation and Interpretation of Temporal Decision Rules. CoRR abs/1004.3334 (2010)
2000 – 2009
- 2009
- [j20]Xing Li, Howard J. Hamilton, Kamran Karimi, Liqiang Geng:
The Multi-Tree Cubing algorithm for computing iceberg cubes. J. Intell. Inf. Syst. 33(2): 179-208 (2009) - [c72]Mohd M. Anwar, Philip W. L. Fong, Xue-Dong Yang, Howard J. Hamilton:
Visualizing Privacy Implications of Access Control Policies in Social Network Systems. DPM/SETOP 2009: 106-120 - 2008
- [j19]Hong Yao, Howard J. Hamilton:
Mining functional dependencies from data. Data Min. Knowl. Discov. 16(2): 197-219 (2008) - [c71]Kamran Karimi, Howard J. Hamilton:
Using Dependence Diagrams to Summarize Decision Rule Sets. Canadian AI 2008: 163-172 - 2007
- [c70]Liqiang Geng, Howard J. Hamilton, Larry Korba:
Expectation Propagation in GenSpace Graphs for Summarization. DaWaK 2007: 449-458 - [c69]Howard J. Hamilton:
Interestingness in Data Mining. EGC 2007: 3 - [p2]Liqiang Geng, Howard J. Hamilton:
Choosing the Right Lens: Finding What is Interesting in Data Mining. Quality Measures in Data Mining 2007: 3-24 - [e4]Fabrice Guillet, Howard J. Hamilton:
Quality Measures in Data Mining. Studies in Computational Intelligence 43, Springer 2007, ISBN 978-3-540-44911-9 [contents] - 2006
- [j18]Liqiang Geng, Howard J. Hamilton:
Interestingness measures for data mining: A survey. ACM Comput. Surv. 38(3): 9 (2006) - [j17]Hong Yao, Howard J. Hamilton:
Mining itemset utilities from transaction databases. Data Knowl. Eng. 59(3): 603-626 (2006) - [j16]Howard J. Hamilton, Liqiang Geng, Leah Findlater, Dee Jay Randall:
Efficient spatio-temporal data mining with GenSpace graphs. J. Appl. Log. 4(2): 192-214 (2006) - [c68]Shannon Blyth, Howard J. Hamilton:
CrowdMixer: Multiple Agent Types in Situation-Based Crowd Simulations. AIIDE 2006: 15-20 - [c67]Mahesh Shrestha, Howard J. Hamilton, Yiyu Yao, Ken Konkel, Liqiang Geng:
The PDD Framework for Detecting Categories of Peculiar Data. ICDM 2006: 562-571 - [c66]Guichong Li, Howard J. Hamilton:
Searching for Pattern Rules. ICDM 2006: 933-937 - 2005
- [j15]Xin Wang, Howard J. Hamilton:
Clustering Spatial Data in The Presence of Obstacles. Int. J. Artif. Intell. Tools 14(1-2): 177-198 (2005) - [j14]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) - [c65]Xin Wang, Howard J. Hamilton:
A Comparative Study of Two Density-Based Spatial Clustering Algorithms for Very Large Datasets. Canadian AI 2005: 120-132 - [c64]Xin Wang, Howard J. Hamilton:
Towards an Ontology-Based Spatial Clustering Framework. Canadian AI 2005: 205-216 - [c63]Howard J. Hamilton, Kamran Karimi:
The TIMERS II Algorithm for the Discovery of Causality. PAKDD 2005: 744-750 - [p1]Hong Yao, Cory J. Butz, Howard J. Hamilton:
Causal Discovery. The Data Mining and Knowledge Discovery Handbook 2005: 945-955 - 2004
- [c62]Liqiang Geng, Howard J. Hamilton:
Finding Interesting Summaries in GenSpace Graphs Efficiently. Canadian AI 2004: 89-104 - [c61]Xin Wang, Howard J. Hamilton:
Clustering Spatial Data in the Presence of Obstacles. FLAIRS 2004: 312-318 - [c60]Xin Wang, Camilo Rostoker, Howard J. Hamilton:
Density-Based Spatial Clustering in the Presence of Obstacles and Facilitators. PKDD 2004: 446-458 - [c59]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 - [c58]Guichong Li, Howard J. Hamilton:
Basic Association Rules. SDM 2004: 166-177 - [c57]Hong Yao, Howard J. Hamilton, Cory J. Butz:
A Foundational Approach to Mining Itemset Utilities from Databases. SDM 2004: 482-486 - 2003
- [j13]Brock Barber, Howard J. Hamilton:
Extracting Share Frequent Itemsets with Infrequent Subsets. Data Min. Knowl. Discov. 7(2): 153-185 (2003) - [j12]Leah Findlater, Howard J. Hamilton:
Iceberg-cube algorithms: An empirical evaluation on synthetic and real data. Intell. Data Anal. 7(2): 77-97 (2003) - [j11]Robert J. Hilderman, Howard J. Hamilton:
Measuring the interestingness of discovered knowledge: A principled approach. Intell. Data Anal. 7(4): 347-382 (2003) - [c56]Kamran Karimi, Howard J. Hamilton:
Discovering Temporal/Causal Rules: A Comparison of Methods. AI 2003: 175-189 - [c55]Linhui Jiang, Howard J. Hamilton:
Methods for Mining Frequent Sequential Patterns. AI 2003: 486-491 - [c54]Kamran Karimi, Howard J. Hamilton:
Distinguishing Causal and Acausal Temporal Relations. PAKDD 2003: 234-240 - [c53]Xin Wang, Howard J. Hamilton:
DBRS: A Density-Based Spatial Clustering Method with Random Sampling. PAKDD 2003: 563-575 - [c52]Cory J. Butz, Hong Yao, Howard J. Hamilton:
A Non-local Coarsening Result in Granular Probabilistic Networks. RSFDGrC 2003: 686-689 - [c51]Howard J. Hamilton, Liqiang Geng, Leah Findlater, Dee Jay Randall:
Spatio-Temporal Data Mining with Expected Distribution Domain Generalization Graphs. TIME 2003: 181-191 - 2002
- [c50]Kamran Karimi, Howard J. Hamilton:
RFCT: An Association-Based Causality Miner. AI 2002: 334-338 - [c49]Howard J. Hamilton, Leah Findlater:
Looking Backward, Forward, and All Around: Temporal, Spatial, and Spatio-Temporal Data Mining. FLAIRS 2002: 481-485 - [c48]Liqiang Geng, Howard J. Hamilton:
ESRS: A Case Selection Algorithm Using Extended Similarity-based Rough Sets. ICDM 2002: 609-612 - [c47]Hong Yao, Howard J. Hamilton, Cory J. Butz:
FD_Mine: Discovering Functional Dependencies in a Database Using Equivalences. ICDM 2002: 729-732 - [c46]Kamran Karimi, Howard J. Hamilton:
TimeSleuth: A Tool for Discovering Causal and Temporal Rules. ICTAI 2002: 375-380 - [c45]Kamran Karimi, Howard J. Hamilton:
Discovering Temporal Rules from Temporally Ordered Data. IDEAL 2002: 25-30 - [c44]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 - [c43]Xin Wang, Christine W. Chan, Howard J. Hamilton:
Design of knowledge-based systems with the ontology-domain-system approach. SEKE 2002: 233-236 - 2001
- [j10]Brock Barber, Howard J. Hamilton:
Parametric Algorithms for Mining Share Frequent Itemsets. J. Intell. Inf. Syst. 16(3): 277-293 (2001) - [c42]Howard J. Hamilton, Xuewei Wang, Y. Y. Yao:
WebAdaptor: Designing Adaptive Web Sites Using Data Mining Techniques. FLAIRS 2001: 128-132 - [c41]Leah Findlater, Howard J. Hamilton:
An Empirical Comparison of Methods for Iceberg-CUBE Construction. FLAIRS 2001: 244-248 - [c40]Robert J. Hilderman, Howard J. Hamilton:
Evaluation of Interestingness Measures for Ranking Discovered Knowledge. PAKDD 2001: 247-259 - 2000
- [c39]Yang Xiang, Xiaohua Hu, Nick Cercone, Howard J. Hamilton:
Learning Pseudo-independent Models: Analytical and Experimental Results. AI 2000: 227-239 - [c38]Robert J. Hilderman, Howard J. Hamilton:
Principles for mining summaries using objective measures of interestingness. ICTAI 2000: 72-81 - [c37]Bradley P. Kram, James A. Hall, Howard J. Hamilton:
Support based measures applied to ice hockey scoring summaries. ICTAI 2000: 352- - [c36]Kamran Karimi, Howard J. Hamilton:
Logical Decision Rules: Teaching C4.5 to Speak Prolog. IDEAL 2000: 85-90 - [c35]Kamran Karimi, Howard J. Hamilton:
Finding Temporal Relations: Causal Bayesian Networks vs. C4.5. ISMIS 2000: 266-273 - [c34]Brock Barber, Howard J. Hamilton:
Parametric Algorithms for Mining Share-Frequent Itemsets. ISMIS 2000: 562-572 - [c33]Brock Barber, Howard J. Hamilton:
Algorithms for Mining Share Frequent Itemsets Containing Infrequent Subsets. PKDD 2000: 316-324 - [c32]Robert J. Hilderman, Howard J. Hamilton:
Applying Objective Interestingness Measures in Data Mining Systems. PKDD 2000: 432-439 - [c31]Kamran Karimi, Julia Ann 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 - [c30]Howard J. Hamilton, Dee Jay Randall:
Data Mining with Calendar Attributes. TSDM 2000: 117-132 - [e3]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. Lecture Notes in Computer Science 1822, Springer 2000, ISBN 3-540-67557-4 [contents]
1990 – 1999
- 1999
- [j9]Avelino J. Gonzalez, Sylvia Daroszewski, Howard J. Hamilton:
Determining the incremental worth of members of an aggregate set through difference-based induction. Int. J. Intell. Syst. 14(3): 275-294 (1999) - [j8]Dee Jay Randall, Howard J. Hamilton, Robert J. Hilderman:
Temporal Generalization with Domain Generalization Graphs. Int. J. Pattern Recognit. Artif. Intell. 13(2): 195-217 (1999) - [j7]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) - [c29]Robert J. Hilderman, Howard J. Hamilton, Brock Barber:
Ranking the Interestingness of Summaries from Data Mining Systems. FLAIRS 1999: 100-106 - [c28]Howard J. Hamilton, Dee Jay Randall:
Heuristic Selection of Aggregated Temporal Data for Knowledge Discovery. IEA/AIE 1999: 714-723 - [c27]Jianna Jian Zhang, Howard J. Hamilton, Nick Cercone:
Learning English Grapheme Segmentation Using the Iterated Version Space Algorithm. ISMIS 1999: 420-429 - [c26]Robert J. Hilderman, Howard J. Hamilton:
Heuristic for Ranking the Interestigness of Discovered Knowledge. PAKDD 1999: 204-209 - [c25]Robert J. Hilderman, Howard J. Hamilton:
Heuristic Measures of Interestingness. PKDD 1999: 232-241 - 1998
- [j6]Robert J. Hilderman, Howard J. Hamilton, Colin L. Carter, Nick Cercone:
Mining Association Rules from Market Basket Data using Share Measures and Characterized Itemsets. Int. J. Artif. Intell. Tools 7(2): 189-220 (1998) - [j5]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) - [c24]Jian Zhang, Howard J. Hamilton:
Learning English Syllabification Rules. Canadian AI 1998: 246-258 - [c23]Dee Jay Randall, Howard J. Hamilton, Robert J. Hilderman:
A Technique for Generalizing Temporal Durations in Relational Databases. FLAIRS 1998: 193-197 - [c22]Avelino J. Gonzalez, Sylvia Daroszewski, Howard J. Hamilton:
Determining the Incremental Worth of Members of an Aggregate Set through Difference-Based Induction. FLAIRS 1998: 245-249 - [c21]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 - [c20]Howard J. Hamilton, Robert J. Hilderman, Liangchun Li, Dee Jay Randall:
Generalization Lattices. PKDD 1998: 328-336 - [c19]Dee Jay Randall, Howard J. Hamilton, Robert J. Hilderman:
Generalization for Calendar Attributes using Domain Generalization Graphs. TIME 1998: 177-184 - 1997
- [j4]Robert J. Hilderman, Howard J. Hamilton:
A Note on Regeneration with Virtual Copies. IEEE Trans. Software Eng. 23(1): 56-59 (1997) - [c18]Howard J. Hamilton, Ning Shan, Wojciech Ziarko:
Machine Learning of Credible Classifications. Australian Joint Conference on Artificial Intelligence 1997: 330-339 - [c17]Ning Shan, Howard J. Hamilton, Nick Cercone:
Inducing and Using Decision Rules in the GRG Knowledge Discovery System. ECML 1997: 234-241 - [c16]Robert J. Hilderman, Liangchun Li, Howard J. Hamilton:
Data Visualization in the DB-Discover System. ICTAI 1997: 474-477 - [c15]Brock Barber, Howard J. Hamilton:
A Comparison of Attribute Selection Strategies for Attribute-Oriented Generalization. ISMIS 1997: 106-116 - [c14]Jian Zhang, Howard J. Hamilton:
Learning English Syllabification for Words. ISMIS 1997: 177-186 - [c13]Colin L. Carter, Howard J. Hamilton, Nick Cercone:
Share Based Measures for Itemsets. PKDD 1997: 14-24 - [c12]Robert J. Hilderman, Howard J. Hamilton, Robert J. Kowalchuk, Nick Cercone:
Parallel Knowledge Discovery Using Domain Generalization Graphs. PKDD 1997: 25-35 - 1996
- [j3]Scott D. Goodwin, Howard J. Hamilton:
It's About Time: An Introduction to the Special Issue on Temporal Representation and Reasoning. Comput. Intell. 12: 357-358 (1996) - [j2]Ning Shan, Howard J. Hamilton, Nick Cercone:
Grg: Knowledge Discovery using Information Generalization, Information Reduction, and Rule Generation. Int. J. Artif. Intell. Tools 5(1-2): 99-112 (1996) - [c11]Brock Barber, Howard J. Hamilton:
Attribute Selection Strategies fro Attribute-Oriented Generalization. AI 1996: 429-441 - [c10]Howard J. Hamilton, Robert J. Hilderman, Nick Cercone:
Attribute-oriented Induction Using Domain Generalization Graphs. ICTAI 1996: 246-253 - [c9]Ning Shan, Howard J. Hamilton, Nick Cercone:
Induction of Classification Rules from Imperfect Data. ISMIS 1996: 118-127 - [c8]Ning Shan, Wojciech Ziarko, Howard J. Hamilton, Nick Cercone:
Discovering Classification Knowledge in Databases Using Rough Sets. KDD 1996: 271-274 - [e2]Luca Chittaro, Scott D. Goodwin, Howard J. Hamilton, Angelo Montanari:
Proceedings of the Third International Workshop on Temporal Representation and Reasoning, TIME-96, Key West, Florida, USA, May 19-20, 1996. IEEE Computer Society 1996, ISBN 0-8186-7528-4 [contents] - 1995
- [j1]Howard J. Hamilton, David R. Fudger:
Estimating DBLEARN's Potential for Knowledge Discovery in Databases. Comput. Intell. 11: 280-296 (1995) - [c7]Ning Shan, Howard J. Hamilton, Nick Cercone:
GRG: knowledge discovery using information generalization, information reduction, and rule generation. ICTAI 1995: 372-379 - [c6]Colin L. Carter, Howard J. Hamilton:
Performance evaluation of attribute-oriented algorithms for knowledge discovery from databases. ICTAI 1995: 486-489 - [c5]Ning Shan, Wojciech Ziarko, Howard J. Hamilton, Nick Cercone:
Using Rough Sets as Tools for Knowledge Discovery. KDD 1995: 263-268 - [c4]Robert J. Hilderman, Howard J. Hamilton:
Performance Analysis of a Regeneration-Based Dynamic Voting Algorithm. SRDS 1995: 196-205 - 1994
- [c3]Scott D. Goodwin, Howard J. Hamilton, Eric Neufeld, Abdul Sattar, André Trudel:
Belief Revision in a Discrete Temporal Probability-Logic. TIME 1994: 113-120 - [e1]Scott D. Goodwin, Howard J. Hamilton:
Proceedings of the TIME-94 International Workshop on Temporal Reasoning, Pensacola, Florida, USA, May 4, 1994. University of Regina, Regina, SK, Canada 1994, ISBN 0-7731-0278-7 [contents] - 1993
- [c2]David R. Fudger, Howard J. Hamilton:
A Heuristic for Evaluating Databases for Knowledge Discovery with DBLEARN. RSKD 1993: 44-51 - 1992
- [c1]Howard J. Hamilton, J. Michael Dyck:
Using the IIPS Framework to Specify Machine-Discovery Problems. ICCI 1992: 266-269
Coauthor Index
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