A novel clustering method with network structure based on clonal algorithm

In the field of cluster analysis, the objective function based clustering algorithm is one of the most widely applied methods. However, this type of algorithm, which needs the priori knowledge about the cluster number and the form of clustering prototypes, can only process data sets with the same type of prototypes. Moreover, these algorithms are very sensitive to the initialization and easy to get trapped into local optima. This paper presents a novel clustering method, with network structure based on a clonal algorithm, to realize the automatization of cluster analysis. By analyzing the neurons of the obtained network with a minimal spanning tree, one can easily get the cluster number and the related classification information. The test results with various data sets illustrate that the novel algorithm achieves more effective performance on cluster analyzing data sets with mixed numeric values and categorical values.