casa 16(9): e5

Research Article

Clustering the objective interestingness measures based on tendency of variation in statistical implications

Download1114 downloads
  • @ARTICLE{10.4108/eai.2-5-2016.151212,
        author={Nghia Quoc Phan and Vinh Cong Phan and Hung Huu Huynh and Hiep Xuan Huynh},
        title={Clustering the objective interestingness measures based on tendency of variation in statistical implications},
        journal={EAI Endorsed Transactions on Context-aware Systems and Applications},
        volume={3},
        number={9},
        publisher={EAI},
        journal_a={CASA},
        year={2016},
        month={5},
        keywords={objective interestingness measures, tendency of variation in statistical implications, distance matrix, Similarity tree, Clustering objective interestingness measures.},
        doi={10.4108/eai.2-5-2016.151212}
    }
    
  • Nghia Quoc Phan
    Vinh Cong Phan
    Hung Huu Huynh
    Hiep Xuan Huynh
    Year: 2016
    Clustering the objective interestingness measures based on tendency of variation in statistical implications
    CASA
    EAI
    DOI: 10.4108/eai.2-5-2016.151212
Nghia Quoc Phan1,*, Vinh Cong Phan2, Hung Huu Huynh3, Hiep Xuan Huynh4
  • 1: Travinh University, Nguyen Thien Thanh Street, Travinh City, Vietnam,
  • 2: Nguyen Tat Thanh University, Nguyen Tat Thanh St., District 4, Ho chi Minh City, Vietnam,
  • 3: Danang University of Science and Technology, Nguyen Luong Bang St, Danang City, Vietnam,
  • 4: Cantho University, 3/2 Street, Ninh Kieu District, Cantho City, Vietnam,
*Contact email: nghiatvnt@gmail.com

Abstract

In recent years, the research cluster of objective interestingness measures has rapidly developed in order to assist users to choose the appropriate measure for their application. Researchers in this field mainly focus on three main directions: clustering based on the properties of the measures, clustering based on the behavior of measures and clustering tendency of variation in statistical implications. In this paper we propose a new approach to cluster the objective interestingness measures based on tendency of variation in statistical implications. In this proposal, we built the statistical implication data of 31 objective interestingness measures based on the examination of the partial derivatives on four parameters. From this data, two distance matrices of interestingness measures are established based on Euclidean and Manhattan distance. The similarity trees are built based on distance matrix that gave results of 31 measures clustering with two different clustering thresholds.