Cluster analysis aims at answering two main questions:how many clusters there are in the data set and where they are located.Usually

the traditional clustering algorithms only focus on the last problem.In order to solve the two problems at the same time

this paper proposes a novel dynamic clustering algorithm called DCBIG

which is based on the immune network and genetic algorithm.The algorithm includes two phases

begins by running immune network algorithm to find a feasible solution

and then employs genetic algorithm to search the optimum number of clusters and the location of each cluster according to the feasible solution.Also

the probabilities and the conditions to acquire a feasible solution through immune network algorithm are discussed in this paper.Experimental results show that new algorithm is characterized by higher convergent probability and convergent speed.