In recent years several approaches to knowledge discovery and data mining, and in particular to clustering, have been developed, but only a few of them are designed for distributed data sources. Distributed clustering model most closely related to statistics is based on distribution models. Clusters can then easily be defined as objects belonging most likely to the same distribution. A nice property of this approach is that this closely resembles the way artificial data sets are generated: by sampling random objects from a distribution. The aim of this paper is to explain Distributed Data Mining (DDM) started to gain attention during the late nineties. Al though it is still a young area of research, the body of literature on DDM constitutes a sizeable portion of the broader data mining literature. DDM in general deals with the problem of finding patterns in an environment where data is either naturally distributed, or could be artificially partitioned across computing nodes. It implies distribution of one or more of: users, data, hardware, or mining software. Centralized data mining systems do not address some the requirements of distributed environments, such as scalability and cooperation. Data mining in distributed environments is known as Distributed Data Mining (DDM), and sometimes as Distributed Knowledge Discovery (DKD).