Please use this identifier to cite or link to this item: https://dspace.ctu.edu.vn/jspui/handle/123456789/4212
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dc.contributor.authorPhan, Quốc Nghĩa-
dc.contributor.authorHuỳnh, Xuân Hiệp-
dc.contributor.authorHuỳnh, Hữu Hưng-
dc.contributor.authorPhan, Công Vinh-
dc.date.accessioned2018-09-14T07:29:38Z-
dc.date.available2018-09-14T07:29:38Z-
dc.date.issued2016-
dc.identifier.urihttp://dspace.ctu.edu.vn/jspui/handle/123456789/4212-
dc.description.abstractIn 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.vi_VN
dc.language.isoenvi_VN
dc.relation.ispartofseriesEAI Endorsed Transactions on Context-aware Systems and Applications;16 .- p.1-8-
dc.subjectObjective interestingness measuresvi_VN
dc.subjectTendency of variation in statistical implicationsvi_VN
dc.subjectDistance matrixvi_VN
dc.subjectSimilarity treevi_VN
dc.subjectClustering objective interestingness measuresvi_VN
dc.titleClustering the objective interestingness measures based on tendency of variation in statistical implicationsvi_VN
dc.typeArticlevi_VN
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