Semantically Enhanced Clustering in Retail Using Possibilistic K-Modes

摘要：

　　Possibility theory can be used to translate numeric values into semantically more meaningful representation with the help of linguistic variables.The data mining applied to a dataset with linguistic variables can lead to results that are easily interpretable due to the inherent semantics in the representation.Moreover,the data mining algorithms based on these linguistic variables tend to orient themselves based on underlying semantics.This paper describes how to transform a real-world dataset consisting of numeric values using linguistic variables based on possibilistic variables.The transformed dataset is clustered using a recently proposed possibilistic k-modes algorithm.The resulting cluster profiles are semantically accessible with very little numerical analysis.

关键词： K-modes method possibility theory retail databases possibility distribution

作者: Asma Ammar Zied Elouedi Pawan Lingras

作者单位: LARODEC, Institut Supérieur de Gestion de Tunis, Université de Tunis 41 Avenue de la Liberté, 2000 L Department of Mathematics and Computing Science, Saint Marys University Halifax, Nova Scotia, B3H 3

会议类型: 国际会议

会议名称: The 9th International Conference on Rough Sets and Knowledge Technology (RSKT 2014)(第九届粗糙集与知识技术国际会议)

会议地点: 上海

会议语种:英文

页码: 753-764

在线出版日期: 2014-10-24（万方平台首次上网日期，不代表论文的发表时间）