Next Article in Journal
Deep Learning-Based Image Segmentation for Al-La Alloy Microscopic Images
Next Article in Special Issue
An Extension of Neutrosophic AHP–SWOT Analysis for Strategic Planning and Decision-Making
Previous Article in Journal
Pre-Rationalized Parametric Designing of Roof Shells Formed by Repetitive Modules of Catalan Surfaces
Previous Article in Special Issue
Generalized Interval Neutrosophic Choquet Aggregation Operators and Their Applications
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessFeature PaperArticle
Symmetry 2018, 10(4), 106; doi:10.3390/sym10040106

Neutrosophic Association Rule Mining Algorithm for Big Data Analysis

1
Department of Operations Research, Faculty of Computers and Informatics, Zagazig University, Sharqiyah 44519, Egypt
2
Math & Science Department, University of New Mexico, Gallup, NM 87301, USA
3
International Business School Suzhou, Xi’an Jiaotong-Liverpool University, Wuzhong, Suzhou 215123, China
*
Authors to whom correspondence should be addressed.
Received: 5 March 2018 / Revised: 29 March 2018 / Accepted: 9 April 2018 / Published: 11 April 2018
View Full-Text   |   Download PDF [9448 KB, uploaded 16 April 2018]   |  

Abstract

Big Data is a large-sized and complex dataset, which cannot be managed using traditional data processing tools. Mining process of big data is the ability to extract valuable information from these large datasets. Association rule mining is a type of data mining process, which is indented to determine interesting associations between items and to establish a set of association rules whose support is greater than a specific threshold. The classical association rules can only be extracted from binary data where an item exists in a transaction, but it fails to deal effectively with quantitative attributes, through decreasing the quality of generated association rules due to sharp boundary problems. In order to overcome the drawbacks of classical association rule mining, we propose in this research a new neutrosophic association rule algorithm. The algorithm uses a new approach for generating association rules by dealing with membership, indeterminacy, and non-membership functions of items, conducting to an efficient decision-making system by considering all vague association rules. To prove the validity of the method, we compare the fuzzy mining and the neutrosophic mining. The results show that the proposed approach increases the number of generated association rules. View Full-Text
Keywords: neutrosophic association rule; data mining; neutrosophic sets; big data neutrosophic association rule; data mining; neutrosophic sets; big data
Figures

Figure 1

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. (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Abdel-Basset, M.; Mohamed, M.; Smarandache, F.; Chang, V. Neutrosophic Association Rule Mining Algorithm for Big Data Analysis. Symmetry 2018, 10, 106.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Symmetry EISSN 2073-8994 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top