抽象的

Modified K-Means Algorithm for Initial Centroid Detection

D. Sharmila Rani, V.T.Shenbagamuthu

Clustering is one of the main analytical methods in data mining. A cluster is a collection of data objects that are similar to one another with in the same cluster and are dissimilar to the objects in other clusters.The process of grouping a set of physical or abstract objects into classes of similar objects is called clustering. In existing system, K-means algorithm proceeds it randomly select k of the objects,each of which initially represents a cluster mean or center. For each of the remaining objects, an object is assigned to the cluster to which it is the most similar, based on the distance between the object and the cluster mean. It then computes the new mean for each cluster.This process iterates the criterion function converges. In our proposed system, requiring a simple data structure to store some information in every iteration,which is to be used in the next iteration.The improved method avoids computing the distance of each data object to the cluster centers repeatedly, saving the running time. Experimental results show that the improved method can effectively improve the speed of clustering and accuracy, reducing the computational complexity of the K-means.