A new clustering algorithm based on artificial immune network and K-means method

This paper proposes a new data clustering method, which is based on artificial immune network and k-means method. With a pool of memory cells, the artificial immune network can be used for estimating the input data distribution, while the k-means method has the capability of shaping clear clusters and obtaining their centers. On the basis of an improved artificial immune network, we first cluster the memory cells by using the k-means algorithm, and with the generated data clusters, we can make the data classification or prediction. The results of our experiments on the standard data sets demonstrate that this new algorithm has a superior performance of data clustering and classification.