The week's pick
Random Articles
Reseach Article
Representation of Concept Hierarchy using an Efficient Encoding Scheme
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 115 - Number 11 |
| Year of Publication: 2015 |
| Authors: Ruchika Yadav, Kanwal Garg, Mittar Vishav |
10.5120/20198-2442
|
Ruchika Yadav, Kanwal Garg, Mittar Vishav . Representation of Concept Hierarchy using an Efficient Encoding Scheme. International Journal of Computer Applications. 115, 11 ( April 2015), 28-32. DOI=10.5120/20198-2442
Abstract
The premise of this paper is to use an efficient encoding scheme which will be used to encode high level concept hierarchy of a transactional table. This table will work as the base to generate multiple level association rules. These rules discovers the hidden knowledge align at higher level of abstraction. Therefore the numeric encoding of the concept hierarchy improves the time complexity and space complexity of task relevant data.
References
- Han, J. , Kamber, M. : Data mining: concepts and techniques. Morgan Kufmann Publisher (2000).
- D. Han, Y. Shi, W. Wang et al. , Research on multi-level association rules based on geosciences data," Journal of Software, vol. 8, no. 12, 3269–3276, 2013.
- Pietro Hiram Guzzi, Marianna Milano and Mario Cannataro, Mining association rules from gene ontology and protein networks: promises and challenges. In Proceeding 14th International Conference on Computational Science, Published by Elsevier Vol. 29, 1970-1980, 2014.
- KW Lin and DJ Deng, A novel parallel algorithm for frequent pattern mining with privacy preserved in cloud computing environments. International journal of Ad Hoc and Ubiquitous Computing, Inderscience publication, 205-215, 2010.
- Annalisa Appice, Margherita Berardi, Michelangelo Ceci, and Donato Malerba, Mining and filtering multi-level spatial association rules with ARES. Proceedings in 15th International Symposium, ISMIS 2005, Saratoga Springs, NY, USA, 342-353, 2005.
- B. Petelin, I. Kononenko, V. Mala?ci?c, and M. Kukar, Multi-level association rules and directed graphs for spatial data analysis. Expert Systems with Applications, vol. 40, no. 12, 4957–4970, 2013.
- H. Han, X. L. Lu, and L. Y. Ren, Using data mining to discover signatures in network-based intrusion detection. Proceedings of the First International Conference on Machine Learning and Cybernetics, Beijing, vol. 1, 2002.
- H. Zhengbing, L. Zhitang, and W. Jumgi, A novel intrusion detection system (NIDS) based on signature search of data mining. WKDD First International Workshop on Knowledge discovery and Data Ming, 10-16, 2008.
- Han, J. : Mining knowledge at multiple concept levels. Proc. 4th Int'l Conf. on Information and Knowledge Management (CIKM'95), Baltimore, Maryland, Nov. (1995) 19–24.
- R. Wille. Concept lattices and conceptual knowledge systems. Computer & Math-ematics with Applications, 23, 493-515, 1992.
- R. S. Michalski. Inductive learning as rule-guided generalization and conceptual simplification of symbolic description: unifying principles and a methodology. Workshop on Current Developments in Machine Learning, Carnegie Mellon University, Pittsburgh, PA, 1980.
- M. Wang and B. Iyer. Ecient roll-up and drill-down analysis in relational database. In 1997 SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery, 39-43, 1997.
- D. Chamberlin. Using the new DB2: IBM's object-relational database system. Morgan Kaufmann, 1996.
- Yijun Lu. Specification, generation and implementation concept hierarchy in data mining. December 1997.
- J. Han and Y. Fu. Discovery of multiple-level association rules from large databases. In Proc. 1995 Int. Conf. Very Large Data Bases (VLDB'95), Zurich , Switzerland, 420-431, 1995.
Index Terms
Keywords