The administration of very large collections of images, accentuates the classical problems of indexing and efficiently querying information. This paper describes a new method applied to very large still image databases that combines two data mining techniques: clustering and association rules mining in order to better organize image collections and to improve the performance of queries. The objective of our work is to exploit association rules discovered by mining, global MPEG-7 features data and to adapt the query processing. In our experiment, we use five MPEG-7 features to describe several thousands of still images. For each feature, we initially determine several clusters of images by using a K-mean algorithm. Then, we generate association rules between different clusters of features and exploit these rules to rewrite the query and to optimize the query-by-content processing.