A New Tree Clustering Algorithm for Fuzzy Data Based on α-cuts
نویسندگان
چکیده
This paper presents a new approach to clustering fuzzy data, called Extensional Tree (ET) clustering algorithm by defining a dendrogram over fuzzy data and using a new metric between fuzzy numbers based on α-cuts. All the similar previous methods extended FCM to support fuzzy data. The present work is based on hierarchical clustering algorithm to cluster fuzzy data. In this novel approach a dendrogram is drawn over fuzzy or crisp data and then the desired clusters are extracted. Finally we compare this approach with some of the newly presented methods in the literature. The major advantage of ET is its fault tolerance against noisy samples. The overall experiments show prominence of our proposed method in comparison with other presented works.
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