References

Howard J. Hamilton


Refereed Journal Papers

    23. Wang, X., Ziebelin, D., and Hamilton, H.J.
    ``An Ontology-Based Framework for Geospatial Clustering,'' International Journal of Geographical Information Systems, Accepted subject to revisions, October 2009. IF5: 2.293.

    22. Karimi, K. and Hamilton, H.J.
    ``Generation and Interpretation of Temporal Decision Rules,'' International Journal of Computational Intelligence Research, Accepted, November 2008.

    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, April 2008, pp. 197-219.

    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.
    ``Title goes here," 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.

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