Interpreting Fuzzy Clustering Results based on Fuzzy Formal Concept Analysis

Minyar Sassi Hidri, A. Touzi, Habib Ounelli
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引用次数: 5

Abstract

The purpose of this paper is to construct structural information from the original data, where the results of fuzzy clustering can be displayed and interpreted. We use fuzzy formal concept analysis (FFCA) based technique for visual data mining and fuzzy clustering results interpretation. The visual interpretation and the navigation in the fuzzy lattice provided useful insights about the overlapping of different clusters and their relationships.
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基于模糊形式概念分析的模糊聚类结果解释
本文的目的是从原始数据中构造结构信息,并在其中显示和解释模糊聚类的结果。我们使用基于模糊形式概念分析(FFCA)的可视化数据挖掘和模糊聚类结果解释技术。模糊格中的视觉解释和导航为不同簇的重叠及其关系提供了有用的见解。
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