A Distance Based Clustering Algorithm (original) (raw)
Clustering is an unsupervised data mining technique used to determine the objects that are similar in characteristics and group them together. K-means is a widely used partitional clustering algorithm but the performance of K-means strongly depends on the initial guess of centers (centroid) and the final cluster centroids may not be the optimal ones. Therefore it is important for K-means to have good choice of initial centroids. We have developed a clustering algorithm based on distance criteria to select a good set of initial centroids. Once some point d is selected as initial centroid, the proposed algorithm computes average of data points to avoid the points near to d from being selected as next initial centroids. These initial centroids are given as input to the K-means technique leading to a clustering algorithm that result in better clustering as compared to the K-means partition clustering algorithm, agglomerative hierarchical clustering algorithm and Hierarchical partitionin...
Loading Preview
Sorry, preview is currently unavailable. You can download the paper by clicking the button above.