A Clustering Method with Efficient Number of Clusters Selected Automatically Based on Shortest Path
Makki Akasha, Ibrahim Musa Ishag, Dong Gyu Lee, Keun Ho Ryu · 2009
Proposed method is for finding optimal number of clusters in large datasets, efficiently without any interventions from user based on relationships among the data objects. The proposed method is divided into two main steps. First is filtering step which uses shortest path between data objects. Second is clustering step which uses mean distance to obtain the number of clusters based on optimal route. The main advantage of this algorithm is its ability to detect the typical number of clusters among objects in datasets. Theoretical analysis and empirical evidence reveal that our method can efficiently self-generate the cluster group automatically rather than other methods. We expect these results to be of interest to researchers and practitioners because it suggests a simple but very elegant and effective alternative for clustering large datasets.