Initialization of Optimized K-means Centroids Using Divide-and-conquer Method

K-means clustering algorithm is one of the most popular unsupervised learning algorithm that is broadly used to clustering the given data items. The k-means algorithm is one of the commonly used clustering methods in data mining. A number of algorithms have been developed for clustering the data items using K-Means due to its simplicity and efficiency. The final clustering result of the K-Means clustering algorithm highly depends upon the initial centroids, which are selected at random by the user. The difficulty of determining “the right number of clusters” in traditional K-Means clustering has attracted significant importance especially in the recent years. There are many improvement were already developed to get better performance of the k-means, but most of these methods needed other inputs like threshold values for the number of data points in a data set. In this work, the proposed algorithm can solve the problems of finding initial centroids and assigning data items to proper clusters using divide-and-conquer method. So in proposed method, the initial cluster centers have obtained using divide-and-conquer property after that K-Means algorithm is applied to gain optimal cluster centers in dataset. The proposed algorithm can improve the execution speed of clustering the data items using little number of iterations. With the help of mathematical calculations the proposed algorithm decreases the complexity which we face in k-means clustering algorithm.