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

被引：2

作者：

Abdullah, Zailani ^{[1
]}

Saman, Md Yazid Mohd ^{[2
]}

Karim, Basyirah ^{[2
]}

Herawan, Tutut ^{[3
]}

Deris, Mustafa Mat ^{[4
]}

Hamdan, Abdul Razak ^{[5
]}

机构：

[1] Univ Malaysia Kelantan, Ctr Comp & Informat, Fac Entrepreneurship & Business, Kota Baharu 16100, Kelantan, Malaysia

[2] Univ Malaysia Terengganu, Sch Informat & Appl Math, Kuala Terengganu 21030, Terengganu, Malaysia

[3] Univ Malaya, Fac Comp Sci & Informat Technol, Kuala Lumpur 50603, Malaysia

[4] Univ Tun Hussein Onn Malaysia, Fac Sci Comp & Informat Technol, Batu Pahat 86400, Johor, Malaysia

[5] Univ Kebangsaan Malaysia, Fac Informat Sci & Technol, Bangi 43600, Selangor, Malaysia

来源：

RECENT ADVANCES ON SOFT COMPUTING AND DATA MINING | 2017年 / 549卷

关键词：

Data mining; Association rule; Formal concept analysis; GALOIS CONCEPT LATTICES; ALGORITHMS;

D O I：

10.1007/978-3-319-51281-5_22

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：213 / 223

页数：11

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