This paper presents a new hierarchical tree approach to clustering fuzzy data, namely extensional tree (ET) clustering algorithm. It defines a dendrogram over fuzzy data and using a new distance between fuzzy numbers based on -cuts. The present work is based on hierarchical clustering algorithm unlike existing methods which improve FCM to support fuzzy data. The Proposed ET clustering algorithm is compared with some of the newly presented methods in the literature. The major advantage of ET, first tree clustering method over fuzzy number, in comparison with other algorithms is its fault tolerance against noisy samples. Some examples prove ability of the proposed ET.

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