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Howard J. Hamilton Vis

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*2008
90EEKamran Karimi, Howard J. Hamilton: Using Dependence Diagrams to Summarize Decision Rule Sets. Canadian Conference on AI 2008: 163-172
89EEHong 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
87EELiqiang 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
85EELiqiang 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
83EEMahesh Shrestha, Howard J. Hamilton, Yiyu Yao, Ken Konkel, Liqiang Geng: The PDD Framework for Detecting Categories of Peculiar Data. ICDM 2006: 562-571
82EEGuichong Li, Howard J. Hamilton: Searching for Pattern Rules. ICDM 2006: 933-937
81EELiqiang Geng, Howard J. Hamilton: Interestingness measures for data mining: A survey. ACM Comput. Surv. 38(3): (2006)
80EEHong Yao, Howard J. Hamilton: Mining itemset utilities from transaction databases. Data Knowl. Eng. 59(3): 603-626 (2006)
79EEHoward 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
78EEXin 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
77EEXin Wang, Howard J. Hamilton: Towards an Ontology-Based Spatial Clustering Framework. Canadian Conference on AI 2005: 205-216
76EEHoward 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
74EEXin Wang, Howard J. Hamilton: Clustering Spatial Data in The Presence of Obstacles. International Journal on Artificial Intelligence Tools 14(1-2): 177-198 (2005)
73EEHoward 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
72EELiqiang 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
70EEXin Wang, Camilo Rostoker, Howard J. Hamilton: Density-Based Spatial Clustering in the Presence of Obstacles and Facilitators. PKDD 2004: 446-458
69EECory J. Butz, Hong Yao, Howard J. Hamilton: Towards Jointree Propagation with Conditional Probability Distributions. Rough Sets and Current Trends in Computing 2004: 368-377
68EEHong Yao, Howard J. Hamilton, Cory J. Butz: A Foundational Approach to Mining Itemset Utilities from Databases. SDM 2004
67EEGuichong Li, Howard J. Hamilton: Basic Association Rules. SDM 2004
2003
66EEKamran Karimi, Howard J. Hamilton: Discovering Temporal/Causal Rules: A Comparison of Methods. Canadian Conference on AI 2003: 175-189
65EELinhui Jiang, Howard J. Hamilton: Methods for Mining Frequent Sequential Patterns. Canadian Conference on AI 2003: 486-491
64EEKamran Karimi, Howard J. Hamilton: Distinguishing Causal and Acausal Temporal Relations. PAKDD 2003: 234-240
63EEXin Wang, Howard J. Hamilton: DBRS: A Density-Based Spatial Clustering Method with Random Sampling. PAKDD 2003: 563-575
62EECory J. Butz, Hong Yao, Howard J. Hamilton: A Non-local Coarsening Result in Granular Probabilistic Networks. RSFDGrC 2003: 686-689
61EEHoward 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)
59EELeah Findlater, Howard J. Hamilton: Iceberg-cube algorithms: An empirical evaluation on synthetic and real data. Intell. Data Anal. 7(2): 77-97 (2003)
58EERobert J. Hilderman, Howard J. Hamilton: Measuring the interestingness of discovered knowledge: A principled approach. Intell. Data Anal. 7(4): 347-382 (2003)
2002
57EEKamran 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
55EELiqiang Geng, Howard J. Hamilton: ESRS: A Case Selection Algorithm Using Extended Similarity-based Rough Sets. ICDM 2002: 609-612
54EEHong Yao, Howard J. Hamilton, Cory J. Butz: FD_Mine: Discovering Functional Dependencies in a Database Using Equivalences. ICDM 2002: 729-732
53EEKamran Karimi, Howard J. Hamilton: TimeSleuth: A Tool for Discovering Causal and Temporal Rules. ICTAI 2002: 375-380
52EEKamran Karimi, Howard J. Hamilton: Discovering Temporal Rules from Temporally Ordered Data. IDEAL 2002: 25-30
51EEY. 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
50EEXin 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
47EERobert 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
44EEYang Xiang, Xiaohua Hu, Nick Cercone, Howard J. Hamilton: Learning Pseudo-independent Models: Analytical and Experimental Results. Canadian Conference on AI 2000: 227-239
43EEBradley P. Kram, James A. Hall, Howard J. Hamilton: Support based measures applied to ice hockey scoring summaries. ICTAI 2000: 352-
42EERobert J. Hilderman, Howard J. Hamilton: Principles for mining summaries using objective measures of interestingness. ICTAI 2000: 72-81
41EEKamran Karimi, Howard J. Hamilton: Logical Decision Rules: Teaching C4.5 to Speak Prolog. IDEAL 2000: 85-90
40EEKamran Karimi, Howard J. Hamilton: Finding Temporal Relations: Causal Bayesian Networks vs. C4.5. ISMIS 2000: 266-273
39EEBrock Barber, Howard J. Hamilton: Parametric Algorithms for Mining Share-Frequent Itemsets. ISMIS 2000: 562-572
38EEBrock Barber, Howard J. Hamilton: Algorithms for Mining Share Frequent Itemsets Containing Infrequent Subsets. PKDD 2000: 316-324
37EERobert J. Hilderman, Howard J. Hamilton: Applying Objective Interestingness Measures in Data Mining Systems. PKDD 2000: 432-439
36EEKamran 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
35EEHoward 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
32EEJianna Jian Zhang, Howard J. Hamilton, Nick Cercone: Learning English Grapheme Segmentation Using the Iterated Version Space Algorithm. ISMIS 1999: 420-429
31EERobert 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
22EEDee Jay Randall, Howard J. Hamilton, Robert J. Hilderman: Generalization for Calendar Attributes using Domain Generalization Graphs. TIME 1998: 177-184
21EEColin 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)
20EERobert 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
19EEHoward J. Hamilton, Ning Shan, Wojciech Ziarko: Machine Learning of Credible Classifications. Australian Joint Conference on Artificial Intelligence 1997: 330-339
18EENing Shan, Howard J. Hamilton, Nick Cercone: Inducing and Using Decision Rules in the GRG Knowledge Discovery System. ECML 1997: 234-241
17EERobert J. Hilderman, Liangchun Li, Howard J. Hamilton: Data Visualization in the DB-Discover System. ICTAI 1997: 474-477
16EEBrock Barber, Howard J. Hamilton: A Comparison of Attribute Selection Strategies for Attribute-Oriented Generalization. ISMIS 1997: 106-116
15EEJian 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
12EERobert 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
9EENing 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
3EEScott 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

Coauthor Index

1Brock Barber [11] [16] [34] [38] [39] [46] [60]
2Shannon Blyth [84]
3Cory J. Butz [54] [62] [68] [69] [75]
4Colin L. Carter [14] [20] [21] [24]
5Nick Cercone [6] [8] [9] [10] [13] [14] [18] [20] [24] [28] [32] [44]
6Christine W. Chan [50]
7Sylvia Daroszewski [25]
8Demyen Doug [73]
9J. Michael Dyck [1]
10Leah Findlater [48] [56] [59] [61] [79]
11David R. Fudger [2] [4]
12Liqiang Geng [55] [61] [72] [79] [81] [83] [85] [87]
13Avelino J. Gonzalez [25]
14Scott D. Goodwin [3] [7]
15Fabrice Guillet [88]
16James A. Hall [43]
17Robert J. Hilderman [5] [10] [12] [13] [17] [20] [22] [23] [24] [26] [28] [29] [30] [31] [34] [37] [42] [47] [58]
18Xiaohua Hu [44]
19Linhui Jiang [65]
20Julia A. Johnson [36]
21Kamran Karimi [36] [40] [41] [52] [53] [57] [64] [66] [76] [90]
22Ken Konkel [83]
23Larry Korba [87]
24Robert J. Kowalchuk [13]
25Bradley P. Kram [43]
26Guichong Li [67] [82]
27Liangchun Li [17] [23]
28Eric Neufeld [3]
29Dee Jay Randall [22] [23] [26] [29] [33] [35] [61] [79]
30Camilo Rostoker [70]
31Abdul Sattar [3]
32Ning Shan [6] [8] [9] [18] [19]
33Mahesh Shrestha [83]
34André Trudel [3]
35Xin Wang [50] [63] [70] [71] [74] [77] [78]
36Xuewei Wang [49] [51]
37Yang Xiang [44]
38Hong Yao [54] [62] [68] [69] [75] [80] [89]
39Yiyu Yao (Y. Y. Yao) [49] [51] [83]
40Jian Zhang [15] [27]
41Jianna Jian Zhang [32]
42Wojciech Ziarko [6] [8] [19]

Colors in the list of coauthors

Copyright © Tue Nov 3 08:52:44 2009 by Michael Ley (ley@uni-trier.de)