FCA-ARMM: A Model for Mining Association Rules from Formal Concept Analysis
Zailani Abdullah, Md Yazid Mohd Saman, Basyirah Karim, Tutut Herawan, Mustafa Mat Deris, Abdul Razak Hamdan · Advances in intelligent systems and computing · 2016
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.