• DocumentCode
    3581199
  • Title

    A rough set multi-knowledge extraction algorithm and its formal concept analysis

  • Author

    Zhengqiong Zhu ; Hui Li ; Guangyao Dai ; Abraham, Ajith ; Wanqing Yang

  • Author_Institution
    Sch. of Inf., Dalian Maritime Univ., Dalian, China
  • fYear
    2014
  • Firstpage
    25
  • Lastpage
    29
  • Abstract
    Rough set theory provides an effective method to reduce attributes and extract knowledge. This paper represents a rough set multi-knowledge extraction algorithm and its formal concept analysis. The proposed algorithm can obtain multi-reducts by using rough set in decision table. The formal concept analysis is used to obtain rules from the main values of the attributes influencing the decision making and these rules build a multi-knowledge. Experimental results show that the proposed multi-knowledge extraction algorithm is efficient.
  • Keywords
    decision making; decision tables; formal concept analysis; knowledge acquisition; rough set theory; attribute reduction; decision making; decision table; formal concept analysis; multireducts; rough set multiknowledge extraction algorithm; Algorithm design and analysis; Niobium; Formal Concept Analysis; Multi-Knowledge; Rough Set; Rule;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Systems Design and Applications (ISDA), 2014 14th International Conference on
  • Print_ISBN
    978-1-4799-7937-0
  • Type

    conf

  • DOI
    10.1109/ISDA.2014.7066261
  • Filename
    7066261
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3581199