Evaluating the Correlation Between Objective Rule Interestingness Measures and Real Human Interest-Reference-Cited by-同舟云学术

Evaluating the Correlation Between Objective Rule Interestingness Measures and Real Human Interest

Published:2005 Issue: Volume: Page:453-461
ISSN:0302-9743
Container-title:Knowledge Discovery in Databases: PKDD 2005
language:
Short-container-title:

Author:

Carvalho Deborah R,Freitas Alex A.,Ebecken Nelson

Publisher

Springer Berlin Heidelberg

Link

http://link.springer.com/content/pdf/10.1007/11564126_45.pdf

Reference9 articles.

1. Carvalho, D.R., Freitas, A.A.: Evaluating Six Candidate Solutions for the Small-Disjunct Problem and Choosing the Best Solution via Meta Learning. AI Review 24(1), 61–98 (2005)

2. Carvalho, D.R., Freitas, A.A., Ebecken, N.F.: A Critical Review of Rule Surprisingness Measures. In: Proc. 2003 Int. Conf. on Data Mining, pp. 545–556. WIT Press (2003)

3. Hilderman, R.J., Hamilton, H.J.: Knowledge Discovery Measures of Interest. Kluwer, Dordrecht (2001)

4. Lecture Notes in Computer Science;F. Hussain,2000

5. Lecture Notes in Artificial Intelligence;M. Ohsaki,2004

Cited by 13 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Using compression to find interesting one-dimensional cellular automata;Complex & Intelligent Systems;2019-09-20

2. Flexible constrained sampling with guarantees for pattern mining;Data Mining and Knowledge Discovery;2017-03-24

3. Multi-agent-based modeling for extracting relevant association rules using a multi-criteria analysis approach;Vietnam Journal of Computer Science;2016-06-10

4. Objectively evaluating condensed representations and interestingness measures for frequent itemset mining;Journal of Intelligent Information Systems;2013-12-17

5. Objectively Evaluating Interestingness Measures for Frequent Itemset Mining;Lecture Notes in Computer Science;2013