Divisive Hierarchical Clustering with K-means and Agglomerative Hierarchical Clustering

To implement divisive hierarchical clustering algorithm with K-means and to apply Agglomerative Hierarchical Clustering on the resultant data in data mining where efficient and accurate result. In Hierarchical Clustering by finding the initial k centroids in a fixed manner instead of randomly choosing them. In which k centroids are chosen by dividing the one dimensional data of a particular cluster into k parts and then sorting those individual parts separately, then the middle elements id in each part is mapped to id of m-dimensional data. The m-dimensional elements whose ids are matched, taken as initial k centroids of any cluster. The applying the Agglomerative Hierarchical Clustering on the resultant each element has its own individual cluster, where the clusters are merger based on the centroid distance. Then finally obtaining k-clusters. A Divisive hierarchical clustering is one of the most important tasks in data mining and this method works by grouping objects into a tree of clusters. The top-down strategy is starting with all objects in one cluster. It subdivides the clusters into smaller and smaller pieces by kmeans algorithm by choosing initial k centroids in a fixed manner to get an efficient result, until each object form a cluster on its own and by applying Agglomerative Hierarchical Clustering on the result to get the efficient k cluster with high accuracy.