Open Access   Article

Fuzzy Hyper-line Segment Neural Network by using Association Rule Mining

B. S. Shetty1 , U. V. Kulkarni2 , Preetee M. Sonule3 , Manisha N. Shinde4

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-12 , Page no. 25-31, Dec-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i12.2531

Online published on Dec 31, 2018

Copyright © B. S. Shetty, U. V. Kulkarni, Preetee M. Sonule, Manisha N. Shinde . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Citation

IEEE Style Citation: B. S. Shetty, U. V. Kulkarni, Preetee M. Sonule, Manisha N. Shinde, “Fuzzy Hyper-line Segment Neural Network by using Association Rule Mining”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.25-31, 2018.

MLA Style Citation: B. S. Shetty, U. V. Kulkarni, Preetee M. Sonule, Manisha N. Shinde "Fuzzy Hyper-line Segment Neural Network by using Association Rule Mining." International Journal of Computer Sciences and Engineering 6.12 (2018): 25-31.

APA Style Citation: B. S. Shetty, U. V. Kulkarni, Preetee M. Sonule, Manisha N. Shinde, (2018). Fuzzy Hyper-line Segment Neural Network by using Association Rule Mining. International Journal of Computer Sciences and Engineering, 6(12), 25-31.

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Abstract

In this paper, we have proposed the fuzzy hyper-line segment neural network (FHLSNN) by using association rule mining(FHLARM). Regression tree is used for pattern recognition. We have used supervised learning neural network classifier for classification of fuzzy sets. The FHLARM make the pattern classification with the help of hyper-line segments. It has two endpoints and corresponding member-ship function. The proposed model is evaluated by using iris, wine and solar mine datasets. For extraction of rules, we have used association rule mining. It gives the better classification accuracy results on various datasets as compared to previous methods. Regression tree maintains a hierarchy of extracting rules.

Key-Words / Index Term

Fuzzy sets, Neural Network, Supervised and unsupervised methods, Pattern classification, FMM

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