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Cluster Validation Analysis on Attribute Relative of Soft-Set Theory Read chapter Read first chapter

2017 | OriginalPaper | Chapter

On Mining Association Rules of Real-Valued Items Using Fuzzy Soft Set

Authors : Dede Rohidin, Noor A. Samsudin, Tutut Herawan

Published in: Recent Advances on Soft Computing and Data Mining

Publisher: Springer International Publishing

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Abstract

Association rules is s one of data mining method that have been implemented in many discipline areas. This rule is able to find interesting relation between the data in a large data set. The traditional association rule has been employed to handle crisp set of items. However, for real-valued items, the traditional association rules fail to handle them. This paper introduces an alternative method for mining association rules for real-valued items. It is based on the concept of hybridization between fuzzy and soft sets. This combination is called fuzzy soft association rules. The results show that the introduced concept was able to mine an interesting association rules among the real number of items where they are represented in fuzzy soft set. Furthermore, it has the ability in dealing with uncertainty or vague data.

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previous chapter The Algorithm Expansion for Starting Point Determination Using Clustering Algorithm Method with Fuzzy C-Means
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Metadata
Title
On Mining Association Rules of Real-Valued Items Using Fuzzy Soft Set
Authors
Dede Rohidin
Noor A. Samsudin
Tutut Herawan
Copyright Year
2017
Book
Recent Advances on Soft Computing and Data Mining

Print ISBN: 978-3-319-51279-2

Electronic ISBN: 978-3-319-51281-5

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-51281-5_52

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