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Erschienen in:
Cluster Validation Analysis on Attribute Relative of Soft-Set Theory Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

FCA-ARMM: A Model for Mining Association Rules from Formal Concept Analysis

verfasst von : Zailani Abdullah, Md Yazid Mohd Saman, Basyirah Karim, Tutut Herawan, Mustafa Mat Deris, Abdul Razak Hamdan

Erschienen in: Recent Advances on Soft Computing and Data Mining

Verlag: Springer International Publishing

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Abstract

The evolution of technology in this era has contributed to a growing of abundant data. Data mining is a well-known computational process in discovering meaningful and useful information from large data repositories. There are various techniques in data mining that can be deal with this situation and one of them is association rule mining. Formal Concept Analysis (FCA) is a method of conceptual knowledge representation and data analysis. It has been applied in various disciplines including data mining. Extracting association rule from constructed FCA is very promising study but it is quite challenging, not straight forward and nearly unfocused. Therefore, in this paper we proposed an Integrated Formal Concept Analysis–Association Rule Mining Model (FCA-ARMM) and an open source tool called FCA-Miner. The results show that FCA-ARMM with FCA-Miner successful in generating the association rule from the real dataset.

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Vorheriges Kapitel Training ANFIS Using Catfish-Particle Swarm Optimization for Classification
Nächstes Kapitel ELP-M2: An Efficient Model for Mining Least Patterns from Data Repository
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Metadaten
Titel
FCA-ARMM: A Model for Mining Association Rules from Formal Concept Analysis
verfasst von
Zailani Abdullah
Md Yazid Mohd Saman
Basyirah Karim
Tutut Herawan
Mustafa Mat Deris
Abdul Razak Hamdan
Copyright-Jahr
2017
Buch
Recent Advances on Soft Computing and Data Mining

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

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

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-51281-5_22