Mining Efficient Fuzzy Bio-Statistical Rules for Association of Sandalwood in Pachaimalai Hills | IGI Global Scientific Publishing
Welcome to the InfoSci Platform
Reference Hub1
Mining Efficient Fuzzy Bio-Statistical Rules for Association of Sandalwood in Pachaimalai Hills

Mining Efficient Fuzzy Bio-Statistical Rules for Association of Sandalwood in Pachaimalai Hills

Delphin Sonia M, John Robinson P, Sebastian Rajasekaran A
Copyright: © 2015 |Volume: 6 |Issue: 2 |Pages: 37
ISSN: 1947-3192|EISSN: 1947-3206|EISBN13: 9781466678705|DOI: 10.4018/ijaeis.2015040104
Cite Article Cite Article

MLA

Sonia M, Delphin, et al. "Mining Efficient Fuzzy Bio-Statistical Rules for Association of Sandalwood in Pachaimalai Hills." IJAEIS vol.6, no.2 2015: pp.40-76. https://doi.org/10.4018/ijaeis.2015040104

APA

Sonia M, D., Robinson P, J., & Rajasekaran A, S. (2015). Mining Efficient Fuzzy Bio-Statistical Rules for Association of Sandalwood in Pachaimalai Hills. International Journal of Agricultural and Environmental Information Systems (IJAEIS), 6(2), 40-76. https://doi.org/10.4018/ijaeis.2015040104

Chicago

Sonia M, Delphin, John Robinson P, and Sebastian Rajasekaran A. "Mining Efficient Fuzzy Bio-Statistical Rules for Association of Sandalwood in Pachaimalai Hills," International Journal of Agricultural and Environmental Information Systems (IJAEIS) 6, no.2: 40-76. https://doi.org/10.4018/ijaeis.2015040104

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

The integration of association rules and correlation rules with fuzzy logic can produce more abstract and flexible patterns for many real life problems, since many quantitative features in real world, especially surveying the frequency of plant association in any region is fuzzy in nature. This paper presents a modification of a previously reported algorithm for mining fuzzy association and correlation rules, defines the concept of fuzzy partial and semi-partial correlation rule mining, and presents an original algorithm for mining fuzzy data based on correlation rule mining. It adds a regression model to the procedure for mining fuzzy correlation rules in order to predict one data instance from contributing more than others. It also utilizes statistical analysis for the data and the experimental results show a very high utility of fuzzy association rules and fuzzy correlation rule mining in modeling plant association problems. The newly proposed algorithm is utilized for seeking close associations and relationships between a group of plant species clustering around Sandalwood in Pachaimalai hills, Eastern Ghats, Tamilnadu.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global Scientific Publishing bookstore.