Density Based Initial Center Optimization Algorithm

Abstract

K-Means is the most popular clustering algorithm with the convergence to one of numerous local minima, which results in much sensitivity to initial representatives and noise point because of its distance based judgment. Density-based clustering algorithm is not sensitive to outliers and noise data, but it's difficult to present the high dimensional data as well as the changes of the data density. Grid-based method is fast, but may reduce the quality and accuracy of the cluster. However, this paper proposes a novel density based initial center optimization algorithm (DBICO) to choose the initial center, which by means of the local optimality and sensitivity of density-based clustering a lgorithm and grid-based method. The core idea is to divide the dataset into several cubes and merge some cubes, delete the noise points according to the density, calculate the initial center point and then clustering the dataset. Doing this can reduce the number of iterations, and avoid the disadvantages of the K- Means algorithm results differ due to the different initial points. Theoretic analysis and experimental demonstrations show that the algorithms this paper proposed outperforms existing algorithms in clustering quality, and it was proved fruitful applications in the logistics.