Clustering in the framework of collaborative agents

We are concerned with data mining in a distributed environment such as the Internet. As sources of data are distributed across the WWW cyberspace, this organization implies a need to develop computing agents exhibiting some form of collaboration. We propose a model of collaborative clustering realized over a collection of datasets in which a computing agent carries out an individual (local) clustering process. The essence of a global search for data structures carried out in this environment deals with a determination of crucial common relationships occurring across the network. Depending upon a way in which datasets are accessible and on a detailed mechanism of interaction, we introduce a concept of horizontal and vertical collaboration. These modes depend upon a way in which datasets are accessed. The clustering algorithms interact between themselves by exchanging information about "local" partition matrices. In this sense, the required communication links are established at the level of information granules (more specifically, fuzzy sets or fuzzy relations forming the partition matrices) rather than data that are directly available in the databases.