References

Howard J. Hamilton



Refereed Journal Papers


32. Laprairie, M., Hamilton, H.J., and Geiger, A.
``ICVG: Practical Constructive Volume Geometry for Indirect Visualization,'' International Journal of Computer Graphics and Animation, 7(3/4), 19 pages, October, 2017.

31. Marcotte, R., and Hamilton, H.J.
``Behavior Trees for Modelling Artificial Intelligence in Games: A Tutorial,'' The Computer Games Journal, 6(3), 171-184. May, 2017.
http://dx.doi.org/10.1007/s40869-017-0040-9.
Link

30. Derzaph, T.L.M., and Hamilton, H.J.
``The PLANI Plant Animation Framework,'' International Journal of Computer Graphics and Animation, 7(1/2), 20 pages, April, 2017.
Online

29. Malik, O.U., Hamilton, H., Hilderman, R., and Dosselmann, R.
``Retail Price Time Series Imputation,'' International Journal of Business Intelligence and Data Mining, 11(1):49-62, 2016.
http://dx.doi.org/10.1504/IJBIDM.2016.076426.

28. Ahmad, H.W., Zilles, S., Hamilton, H.J., and Dosselmann, R.
``Prediction of Retail Prices of Products Using Local Competitors,'' International Journal of Business Intelligence and Data Mining, 11(1):19-30, 2016.
http://dx.doi.org/10.1504/IJBIDM.2016.076418.

27. Schroeder, D., and Hamilton, H.J.
``Desirable Elements for a Particle System Interface,'' International Journal of Computer Games Technology, 2014, Article ID 623809, 2014, 12 pages.
http://www.hindawi.com/journals/ijcgt/2014/. http://dx.doi.org/10.1155/2014/623809.

26. Derzaph, T.L.M., and Hamilton, H.J.
``Effects of Wind on Virtual Plants in Animation,'' International Journal of Computer Games Technology, 2013, Article ID 674848, 2013, 11 pages.
http://www.hindawi.com/journals/ijcgt/2013/674848/.

25. Targett, S., Verlysdonk, V., Hamilton, H.J., Hepting, D.
``A Study of User Interface Modifications in World of Warcraft,'' Game Studies, 12(2), 2012.
http://gamestudies.org/1202/articles/ui_mod_in_wow.

24. Wang, X., Rostoker, C., and Hamilton, H.J.
``A Density-Based Spatial Clustering Method for Physical Constraints,'' Journal of Intelligent Information Systems, 38(1):269-297, 2012. DOI:10.1007/s10844-011-0154-7.

23. Karimi, K. and Hamilton, H.J.
``Generation and Interpretation of Temporal Decision Rules,'' International Journal of Computer Information Systems and Industrial Management Applications, 3:314-323, 2011.
http://www.mirlabs.org/ijcisim/

22. Wang, X., Gu, W., Ziebelin, D., and Hamilton, H.J.
``An Ontology-Based Framework for Geospatial Clustering,'' International Journal of Geographical Information Science, 24(11):1601-1630, 2010. IF 1.533, IF5: 2.303.

21. Li, X., Hamilton, H.J., Karimi, K., and Geng, L.
``The Multi-Tree Cubing Algorithm for Computing Iceberg Cubes,'' Journal of Intelligent Information Systems, 33(2), October 2009, pp. 179-208. http://dx.doi.org/10.1007/s10844-008-0074-3

20. Yao, H. and Hamilton, H.J.
``Mining Function Dependencies from Data with FD_Mine,'' Data Mining and Knowledge Discovery, Mining functional dependencies from data, 16(2):197-219, 2008. DOI 10.1007/s10618-007-0083-9.

19. Yao, H., and Hamilton, H.J.
``Mining Itemset Utilities from Transaction Databases,'' Data and Knowledge Engineering, 59(3), December 2006, pp. 603-626.

18. Geng, L., and Hamilton, H.J.
``Interestingness Measures for Data Mining: A Survey,'' ACM Computing Surveys, 38(3), September 2006, Article 9.

17. Hamilton, H.J., Geng, L., Findlater, L., and Randall, D.J.
``Efficient Spatio-Temporal Data Mining with GenSpace Graphs,'' Journal of Applied Logic, 4(2):192-214, 2006.

16. Wang, X., and Hamilton, H.J.
``Clustering Spatial Data in the Presence of Obstacles," International Journal of Artificial Intelligence Tools, 14(1 & 2):177-198, February & April, 2005.

15. Hamilton, H.J., and Demyen, D.
``A Machine-Discovery Approach to the Evaluation of Hashing Techniques,'' Journal of Experimental and Theoretical Artificial Intelligence, 17(1-2), January-June 2005, pp. 45-62.

14. Hilderman, R.J., and Hamilton, H.J.
``Measuring the Interestingness of Discovered Knowledge: A Principled Approach,'' Intelligent Data Analysis, 7(4):347-382, 2003.

13. Findlater, L., and Hamilton, H.J.
``Iceberg Cube Algorithms: An Empirical Evaluation on Synthetic and Real Data,'' Intelligent Data Analysis, 7(2):77-97, 2003.

12. Barber, B., and Hamilton, H.J.
``Extracting Share Frequent Itemsets with Infrequent Subsets,'' Data Mining and Knowledge Discovery, 7(2):153-185, April 2003.

11. Barber, B., and Hamilton, H.J.
``Parametric Algorithms for Mining Share Frequent Itemsets,'' Journal of Intelligent Information Systems, 16(3):277-293, August, 2001.

10. Hilderman, R.J., Hamilton, H.J., and Cercone, N.
``Data Mining in Large Databases Using Domain Generalization Graphs,'' Journal of Intelligent Information Systems, 13(3):195-234, November/December, 1999.

9. Gonzalez, A.J., Daroszewski, S., and Hamilton, H.J.
``Determining the Incremental Worth of Members of an Aggregate Set through Difference-Based Induction," International Journal of Intelligent Systems, 14(3):275-294, March, 1999.

8. Randall, D.J., Hamilton, H.J., and Hilderman, R.J.
``Temporal Generalization with Domain Generalization Graphs," International Journal of Pattern Recognition and Artificial Intelligence. 13(2):195-217, 1999.

7. Hilderman, R.J., Carter, C.L., Hamilton, H.J., and Cercone, N.
``Mining Association Rules from Market Basket Data using Share Measures and Characterized Itemsets," International Journal of Artificial Intelligence Tools. 7(2):189-220, June, 1998.

6. Carter, C.L. and Hamilton, H.J.,
``Efficient Attribute-Oriented Algorithms for Knowledge Discovery from Large Databases,'' IEEE Trans. on Knowledge and Data Engineering. 10(2):193-208, March/April, 1998.

5. Hilderman, R.J. and Hamilton, H.J.,
``A Note on Regeneration with Virtual Copies,'' IEEE Trans. on Software Engineering. 23(1):56-59, January, 1997.

4. Shan, N., Hamilton, H.J., and Cercone, N.J.,
``GRG: Knowledge Discovery Using Information Generalization, Information Reduction, and Rule Generation,'' International Journal of Artificial Intelligence Tools. 5:(1 & 2), 1996, pp. 99-112.

3. Hamilton, H.J. and Fudger, D.F.,
"Estimating DBLEARN's Potential for Knowledge Discovery in Databases," Computational Intelligence, 11:2, 1995, pp. 280-296.

2. Carter, C.L. and Hamilton, H.J.,
"A Fast, On-line Generalization Algorithm for Knowledge Discovery," Applied Mathematics Letters, 8:2, 1995, pp. 5-11.

1. Hamilton, H.J. and Dyck, J.M.,
"IIPS: A Framework for Specifying Inductive-Inference Problems," Applied Mathematics Letters, 5:6, 1992, pp. 89-94.

Other References:

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