Combining Clustering techniques and Formal Concept Analysis to characterize Interestingness Measures
作者： Engelbert Mephu Nguifo , Dhouha Grissa , Sylvie Guillaume
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摘要: Formal Concept Analysis "FCA" is a data analysis method which enables to discover hidden knowledge existing in data. A kind of extracted from association rules. Different quality measures were reported the literature extract only relevant Given dataset, choice good measure remains challenging task for user. evaluation matrix according semantic properties, this paper describes how FCA can highlight with similar behavior order help user during his choice. The aim article discovery Interestingness Measures "IM" clusters, able validate those found due hierarchical and partitioning clustering methods "AHC" "k-means". Then, based on theoretical study sixty one interestingness nineteen proposed recent study, several groups measures.
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128.84.21.199参考文章(22)
Mitsunori Ogihara, Mohammed J. Zaki, Theoretical Foundations of Association Rules ,(2007)
Daniel Rajaonasy Feno, Mesures de qualité des règles d'association : normalisation et caractérisation des bases Université de la Réunion. ,(2007)
Robert J. Hilderman, Howard J. Hamilton, Knowledge discovery and measures of interest ,(2001)
Ramakrishnan Srikant, Rakesh Agrawal, Fast algorithms for mining association rules very large data bases. pp. 580- 592 ,(1998)
Jun Sese, Shinichi Morishita, Answering the Most Correlated N Association Rules Efficiently european conference on principles of data mining and knowledge discovery. pp. 410- 422 ,(2002) , 10.1007/3-540-45681-3_34
Bernhard Ganter, Rudolf Wille, C. Franzke, Formal Concept Analysis: Mathematical Foundations ,(1998)
Gregory Piatetsky-Shapiro, Discovery, Analysis, and Presentation of Strong Rules Knowledge Discovery in Databases. pp. 229- 238 ,(1991)
Mondher Maddouri, Jamil Gammoudi, On Semantic Properties of Interestingness Measures for Extracting Rules from Data international conference on adaptive and natural computing algorithms. pp. 148- 158 ,(2007) , 10.1007/978-3-540-71618-1_17
Liqiang Geng, Howard J. Hamilton, Choosing the Right Lens: Finding What is Interesting in Data Mining Quality Measures in Data Mining. pp. 3- 24 ,(2007) , 10.1007/978-3-540-44918-8_1
Rudolf Wille, RESTRUCTURING LATTICE THEORY: AN APPROACH BASED ON HIERARCHIES OF CONCEPTS international conference on formal concept analysis. pp. 314- 339 ,(2009) , 10.1007/978-3-642-01815-2_23