Interactive Evolutionary Computation and density-based clustering for data analysis

Abstract

Data clustering is useful in solving many pattern recognition and decision support tasks. This work has empirically demonstrated the effectiveness of a hybrid neural network model for density-based clustering. The cluster regions formed were then evaluated based on visualisation of clustering information on the map. The visual inspection of the map revealed the number of clusters as well as their spatial relationships. By analysing the clustering information in this way, the cluster (or density) structures of the data were obtained. In this paper, a case study of pen-based handwritten digits recognition was chosen to demonstrate how, in this by using the interactive evolutionary computational (IEC), both the computer system and the user work together in the cluster analysis process and subsequently, shown that this approach is suitable for exploratory data analysis.