A Quick Reduction Algorithm Based on Important Index

Article Preview

Abstract:

This paper tries to find a more feasible method to achieve core and reduction. Against concepts "distinguishable relation of attribute set" and "distinguishable unit set of attribute set", it defines a concept "important index", and proposes an effective and quick approach for important index. After drawn out the involved theory and equivalent proposition, also presents algorithms for core and reduction upon the important index. The heuristic reduction algorithm adopts the bottom-up design, and gets reduction based on the heuristic information "important index of attribute set". The complexity of the algorithm in space is O(m), and the complexity in time is O(mn2). The theoretical analysis and results show that the ways proposed here simplify the relevant operations and are suitable to deal with the huge volume of data.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

13-17

Citation:

Online since:

December 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2015 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] Z. Pawlak: International Journal of Computer and Information Sciences Vol. 11 (1982), pp.341-356.

Google Scholar

[2] Z. Pawlak: Information Sciences Vol. 147 (2002), pp.1-12.

Google Scholar

[3] S.K.M. Wong and W. Ziarko: Bulletin of Polish Academy of Sciences Vol. 33 (1985), pp.693-696.

Google Scholar

[4] Xiaohua Hu: Knowledge Discovery in Databases: An Attribute-oriented Rough Set Approach (Ph. D. Thesis, Canada: University of regina, 1995).

Google Scholar

[5] Keyun Hu: Research on Concept Lattice and Rough Set Based Data Mining Methods (Ph. D Thesis, Tsinghua University, 2001, In Chinese).

Google Scholar

[6] Yiyu Yao and Yan Zhao: Information Sciences Vol. 178 (2008), p.3356–3372.

Google Scholar

[7] Dongyi Ye and Zhaojiong Chen: International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems Vol. 18 (2010), p.209–222.

Google Scholar

[8] R. Jensen and Q. Shen: IEEE Transactions on Knowledge and Data Engineering Vol. 16 (2004), p.1457–1471.

Google Scholar

[9] Q. Shen and R. Jensen: Pattern Recognition Vol. 37 (2004), p.1351–1363.

Google Scholar

[10] Xinying Chen and Zhanzhi Qiu: Computer Engineering Vol. 36 (2010), pp.53-55 (In Chinese).

Google Scholar

[11] SGI-MLC++: datasets from UCI on http: /www. sgi. com/tech/mlc/db/, (2006).

Google Scholar