Association rule mining has recently attracted strong attention and proven to be a highly successful technique for extracting useful information from very large databases. In this paper, we explore a generalized affinity-based association mining which discovers quasi-equivalent media objects in a distributed information-providing environment consisting of a network of heterogeneous databases which could be relational databases, hierarchical databases, object-oriented databases, multimedia databases, etc. Online databases, consisting of millions of media objects, have been used in business management, government administration, scientific and engineering data management, and many other applications owing to the recent advances in high-speed communication networks and large-capacity storage devices. Because of the navigational characteristic, queries in such an information-providing environment tend to traverse equivalent media objects residing in different databases for the related data records. As the number of databases increases, query processing efficiency depends heavily on the capability to discover the equivalence relationships of the media objects from the network of databases. Theoretical terms along with an empirical study of real databases are presented.