Abstract: According to the No Free Lunch theory，no clustering algorithm can solve all problems，and it is difficult for users to select a suitable algorithm when a number of clustering algorithms are available.An algorithm recommendation system can be a potential solution.In this paper，we propose a framework of clustering algorithm recommendation.Firstly，a dataset and an algorithm library are constructed respectively，and the mapping relationship between the datasets and the algorithms is established by evaluating the performance of the algorithms on the datasets.Then we devise a similarity measure of dataset by calculating the statistical characteristics，binary vector，compactness and connectedness attribute of the datasets and weighting the attributes with BP network.For the input dataset，we find the most similar one in the dataset library by the similarity measure.Finally，the recommended clustering algorithm can be achieved according to the mapping relationship between the datasets and algorithms.In the proposed framework，eight artificial datasets and eight UCI real datasets are selected to construct the datasets library，and seven representative clustering algorithms are used to form the algorithm library.The experiments on some UCI datasets demonstrate the proposed recommendation framework is with satisfact performance.