Grid Clustering with Genetic Algorithm and Tabu Search Process

In this paper we have presented an effective hybrid genetic algorithm for solving clustering problems with multi-dimensional grid structure. The algorithm is basically a combination of Genetic Algorithm (GA) and Tabu Search (TS) so that we can efficiently utilize the stochastic search ability of GA and the hill climbing as well as the local search capabilities of TS. Such hybridization helps to enhance the capability of both the search techniques and to reduce their disadvantages. The application of TS along with GA also greatly reduces the possibility of a stochastic search process to be trapped in a local optimal solution. The proposed grid structure based clustering method is a two-step process. It starts with decomposing the data set into a finite number of grid cells. At the end of the grid partitioning process, each non-empty grid cell is considered to be a small cluster or sub-cluster. In the second step, the hybrid genetic algorithm is invoked to merge the sub-clusters hierarchically and iteratively so that the expected set of k clusters is finally obtained. The performance of this new technique has been tested on various multi-dimensional synthetic and real data sets. A comparison with related clustering and classification techniques have also been made on some data sets.