This paper discusses a symbolic clustering method for distribution valued dissimilarities. Symbolic Data Analysis (SDA) is a new approach for data analysis proposed by Diday in 1980s. Especially, a clustering method for symbolic data is called "Symbolic clustering". There are a lot of researches including Hierarchical clustering by Bock (2001) and Chavent & Lechecallier (2002), but there are not so many researches dealing with distribution valued dissimilarities. This paper proposes a new method for symbolic clustering using distribution valued dissimilarities.