A two-pass clustering algorithm based on linear assignment initialization and k-means method

This paper presents a two-pass clustering algorithm with a combination of the linear assignment and k-means methods. To avoid the inconsistency of clustering results from the k-means method with random initialization, the linear assignment method with the least similar cluster representatives is applied first to generate initial clusters, and then followed with the k-means method. This approach is applied to four well-known practical UCI datasets. The results are compared with those from the k-means method with other random initialization approaches and it is shown that the two pass approach consistently results in the best clustering results. The application results on color image segmentation are also demonstrated.