FCA-ARMM: A Model for Mining Association Rules from Formal Concept Analysis
| Autor: | Mustafa Mat Deris, Basyirah Karim, Tutut Herawan, Abdul Razak Hamdan, Yazid Mohd Saman, Zailani Abdullah |
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| Rok vydání: | 2016 |
| Předmět: |
Knowledge representation and reasoning
Association rule learning Computer science Process (engineering) 0211 other engineering and technologies 0202 electrical engineering electronic engineering information engineering Formal concept analysis 020201 artificial intelligence & image processing 021107 urban & regional planning 02 engineering and technology Data mining computer.software_genre computer |
| Zdroj: | Advances in Intelligent Systems and Computing ISBN: 9783319512792 SCDM |
| DOI: | 10.1007/978-3-319-51281-5_22 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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