Dynamic partitional clustering using multi-agent technology

Most of the well established clustering algorithms assume that the underlying clustering structure of dataset does not change over the time. Hence, those algorithms fail to identify underlying cluster structures in currently available large scale dynamic data sources in an efficient manner. This paper presents a Multi Agent based approach to identify partitional clusters in a dynamic data source. Set of partitional clusters in a dynamic data source is identified by interactions and negotiations among the agents who represent data records in the data source. After identification of potential clusters for data records that are assigned to what are called cluster agents. By interactions and negotiations between cluster agents and data record agents, the identified cluster configuration is continuously improved according to the internal cluster evaluation measures. The proposed method is evaluated by synthetic data sets with different number of clusters in 2D and 3D spaces. Results indicate that the proposed method successfully identifies the clusters in those datasets with minimal human intervention.