R.J. Hilderman and H.J. Hamilton. Evaluation of interestingness measures for ranking discovered knowledge. In Williams G.J. Cheung, D. and Q. Li, editors, Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'01), Lecture Notes in Computer Science, pages 247--259, Hong Kong, April 2001. Springer-Verlag. Home/Search Document Details and Download
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Selecting the Right Interestingness Measure for Association.. - Tan, Kumar (2002) (25 citations) (Correct)
....tables such that finding the most appropriate measure using this small set of tables is almost equivalent to finding the best measure using the entire data set. The problem of evaluating objective measures used by data mining algorithms has attracted considerable attention in recent years [7, 6, 10]. For example, Kononenko et al. 10] have examined the use of di#erent impurity functions for top down inductive decision trees while Hilderman et al. 7, 6] have conducted extensive studies on the behavior of various diversity measures for ranking data summaries generated by attribute oriented ....
....set. The problem of evaluating objective measures used by data mining algorithms has attracted considerable attention in recent years [7, 6, 10] For example, Kononenko et al. 10] have examined the use of di#erent impurity functions for top down inductive decision trees while Hilderman et al. [7, 6] have conducted extensive studies on the behavior of various diversity measures for ranking data summaries generated by attribute oriented generalization methods. The specific contributions of this paper are: We present an overview of various measures proposed in the statistics, machine ....
[Article contains additional citation context not shown here]
R. Hilderman and H. Hamilton. Evaluation of interestingness measures for ranking discovered knowledge. In Proc. of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'01), pages 247--259, Hong Kong, April 2001.
Selecting the Right Interestingness Measure for Association.. - Tan, Kumar, al. (2002) (25 citations) (Correct)
....tables such that finding the most appropriate measure using this small set of tables is almost equivalent to finding the best measure using the entire data set. The problem of evaluating objective measures used by data mining algorithms has attracted considerable atten tion in recent years [7, 6, 10]. For example, Kononenko et al. 10] have examined the use of different impurity func tions for top down inductive decision trees while Hilderman et al. 7, 6] have conducted extensive studies on the behav ior of various diversity measures for ranking data summaries generated by ....
.... The problem of evaluating objective measures used by data mining algorithms has attracted considerable atten tion in recent years [7, 6, 10] For example, Kononenko et al. 10] have examined the use of different impurity func tions for top down inductive decision trees while Hilderman et al. [7, 6] have conducted extensive studies on the behav ior of various diversity measures for ranking data summaries generated by attribute oriented generalization methods. The specific contributions of this paper are: We present an overview of various measures proposed in the statistics, machine ....
[Article contains additional citation context not shown here]
R. Hilderman and H. Hamilton. Evaluation of interestingness measures for ranking discovered knowledge. In Proc. of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'01), pages 24259, Hong Kong, April 2001.
Algorithms for Association Rules - Hegland (2002) (Correct)
....considered constraints in order to select more interesting rules but their e#ect is limited, moreover, such constraints may not be available. Thus the usage of additional interestingness measures (which act as filters) has received considerable discussion in the datamining literature, see, e.g. [5]. There is, however, a large deal of redundancy in the set of frequent itemsets and we will consider here the removal of this redundancy, e#ectively compressing the set of frequent itemsets. The tool used for this compression is the apriori principle. The second computational problem relates not ....
Hilderman, R.J., Hamilton, H.J.: Evaluation of interestingness measures for ranking discovered knowledge. Lecture Notes in Computer Science 2035 (2001) 247--??
DeEPs: A New Instance-based Discovery and Classification System - Li, Dong (2001) (1 citation) (Correct)
....no or almost no occurrence in the negative data. Ranking discovered patterns is an intensively studied topic in data mining, the readers are referred to (Klemettinen et al. 1994; Silberschatz Tuzhilin, 1996; Dong Li, 1998; Padmanabhan Tuzhilin, 1998; Bayardo Agrawal, 1999; Sahar, 1999; Hilderman Hamilton, 2001) for other subjective and objective measurements originated in information theory, statistics, ecology, and economics. 8 Performance Evaluation: Accuracy, Speed, and Scalability We report in this section the performance of our method in comparison to the performance of k NN and C5.0. We used 40 ....
Hilderman, R. J., & Hamilton, H. J. (2001). Evaluation of interestingness measures for ranking discovered knowledge. Proceedings of the Fifth Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 247--259). Hong Kong, China: Springer-Verlag.
Integrating and Updating Domain Knowledge - With Data Mining (Correct)
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R.J. Hilderman and H.J. Hamilton. Evaluation of interestingness measures for ranking discovered knowledge. In Williams G.J. Cheung, D. and Q. Li, editors, Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'01), Lecture Notes in Computer Science, pages 247--259, Hong Kong, April 2001. Springer-Verlag.
Integrating and Updating Domain Knowledge with Data Mining - Pohle (Correct)
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R.J. Hilderman and H.J. Hamilton. Evaluation of interestingness measures for ranking discovered knowledge. In Williams G.J. Cheung, D. and Q. Li, editors, Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'01), Lecture Notes in Computer Science, pages 247--259, Hong Kong, April 2001. Springer-Verlag.
A System for Detecting Privacy and Ethical Sensitivity in Data .. - Roddick, Fule (2003) (Correct)
No context found.
Robert J. Hilderman and Howard J. Hamilton. Evaluation of interestingness measures for ranking discovered knowledge. In David Wai-Lok Cheung, Graham J. Williams, and Qing Li, editors, 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining - PAKDD 2001.
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