A Structural Analysis based on Similarity between Fuzzy Clusters and its Application to Evaluation Data
A Structural Analysis based on Similarity between Fuzzy Clusters and its Application to Evaluation Data
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基于模糊聚类相似度的结构分析及其在评价数据中的应用
DOI：
10.1007/978-3-642-29977-3_8
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
M. Sato-Ilic
中科院分区：
文献类型：
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作者：
R. Chiba;T. Furutani;M. Sato-Ilic
提出了一种基于模糊聚类相似度的消费者偏好评价数据分析方法。我们将此方法应用于多种类型的评估数据。该方法的优点是捕捉消费者偏好的模糊聚类的潜在结构和这些模糊聚类之间的关系作为相似性。另外，由于不同行业之间存在相同的模糊聚类，因此可以利用模糊聚类的标度，通过对同一主题的评价来比较行业之间的差异。我们显示了更好的性能，从使用我们提出的方法与几个数值例子。
This paper presents a similarity of fuzzy clusters based analysis for consumer preference evaluation data. We apply this method to multiple types of evaluation data. The merit of this method is to capture the latent structure of consumer preferences represented by fuzzy clusters and the relation between these fuzzy clusters as similarity. In addition, due to the presence of identical fuzzy clusters over the different industries, we can compare the difference between the industries through the same subject evaluation by using the scale of the fuzzy clusters. We show a better performance from the use of our proposed method with several numerical examples.