casa 16(10): e4

Research Article

Classification of objective interestingness measures

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  • @ARTICLE{10.4108/eai.12-9-2016.151678,
        author={Lan Phuong Phan and Nghia Quoc Phan and Vinh Cong Phan and Hung Huu Huynh and Hiep Xuan Huynh and Fabrice Guillet},
        title={Classification of objective interestingness measures},
        journal={EAI Endorsed Transactions on Context-aware Systems and Applications},
        keywords={objective interestingness measures, classification, property/criterion of interestingness measures, association rules.},
  • Lan Phuong Phan
    Nghia Quoc Phan
    Vinh Cong Phan
    Hung Huu Huynh
    Hiep Xuan Huynh
    Fabrice Guillet
    Year: 2016
    Classification of objective interestingness measures
    DOI: 10.4108/eai.12-9-2016.151678
Lan Phuong Phan1,*, Nghia Quoc Phan2, Vinh Cong Phan3, Hung Huu Huynh4, Hiep Xuan Huynh1, Fabrice Guillet5
  • 1: Can Tho University, Campus 2- 3/2 Street, Ninh Kieu District, Can Tho City, Vietnam
  • 2: Tra Vinh University, No. 126 National Road 53, Ward 5, Tra Vinh City, Vietnam
  • 3: Nguyen Tat Thanh University, 300A Nguyen Tat Thanh Street, Ward 13, District 4, Ho Chi Minh City, Vietnam
  • 4: VNUK Institute for Research and Executive Education, The University of Danang, 41 Le Duan Street, Hai Chau District, Danang city, Vietnam
  • 5: Polytech Nantes, University of Nantes, La Chantrerie rue Christian Pauc BP 50609 F-44306 Nantes Cedex 3, France
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The creation of the interestingness measures for evaluating the quality of the association rule - based knowledge plays an important role in the post-processing of the Knowledge Discovery from Databases. More and more interestingness measures are proposed by two approaches (subjective assessment and objective assessment), studying the properties or the attributes of the interestingness measures is important in understanding the nature of the objective interestingness measures. In this paper, we focus primarily on the objective interestingness measures to obtain a general view of recent researches on the nature of the objective interestingness measures, as well as complete a new classification on 109 selected objective interestingness measures on 6 criterions (independence, equilibrium, symmetry, variation, description, and statistics).