Selecting the Dataset for Classification using Predictive Apriori and Diversity Measures
Abstract
Keywords
Full Text:
PDFReferences
Rakesh Agrawal and Ramakrishnan Srikant.” Fast algorithms for mining association rules in large databases” Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, pages 487-499, Santiago, Chile, September 1994.
Stefan Mutter, “Classification using Association rules”, A thesis of Diploma of computer science, Univeristy of Freiburg, Hamilton, NewZealoand,Aotearoa, 11th march 2004.
Huebner, Richard A, “ Diversity-based interestingness measures for association rule mining “ Proceedings of ASBBS Annual conference “
Las Vegas , Vol 16, No.1
Geng,L., & Hamilton, H.J (2006). Interestingness measures of Data mining: A survey. ACM Computing surveys 38(3), Article 5.
S. Brin, R. Motwani, and C. Silverstein. Beyond market baskets: Generalizing association rules to correlations. SIGMOD Record (ACM Special Interest Group on Management of Data), 26(2):265, 1997
D. Lin and Z. Kedem. Pincer-search: A new algorithm for discovering the maximum frequent set. In 6th Intl. Conf. Extending Database Technology, March 1998.
Zaki. Generating non-redundant association rules. In 6th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, August 2000
C. Perng, H. Wang, S. Ma and J. Hellerstein. Discovery in Multi-attribute Data with User-defined Constraints, ACM SIGKDD Explorations Newsletter, Volume4, Issue 1, Pages: 56 - 64, June 2002
Sergio A. Alvarez,”Chi-squared computation for association rules : Preliminary results” Technical Report BC-CS-2003-01 July 2003.
Sotiris Kotsiantis, Dimitris Kanellopoulos, “Association Rules Mining: A Recent Overview”, GESTS International Transactions on Computer Science and Engineering, Vol.32 (1), 2006, pp. 71-82
Liqiang gengand Howard J. Hamilton, “Interstingness measures for Data mining : A Survey “,ACM Computing surveys, vol 38,No.3,Article 9, September 2006.
Jiuyong Li. On optimal rule discovery. IEEE Transactions on Knowledge and Data Engineering, 18(4):460-471, 2006.
Nimrod Megiddo and Ramakrishnan Srikant. Discovering predictive association rules.”Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD-98)”, pages 274-278. AAAI Press, 1998.
http://archive.ics.uci.edu/ml/datasets
weka tool– open data mining tool
Jiawei Han and Micheline Kamber , “ Data Mining “- concepts and Techniques, II Edition , Elesevier Publicaitons.
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.