Comparative Analysis of BIRCH and CURE Hierarchical Clustering Algorithm using WEKA 3.6.9

Abstract

Hierarchical Clustering is the process of forming a maximal collection of subsets of objects (called clusters), with the property that any two clusters are either disjoint or nested. Hierarchical clustering combine data objects into clusters, those clusters into larger clusters, and so forth, creates a hierarchy of clusters, which may represent a tree structure called a dendrogram, in which the root of the tree consists of a single cluster containing all observations, and the leaves correspond to individual observations. BIRCH and CURE are two integrated hierarchical clustering algorithm. These are not pure hierarchical clustering algorithm, some other clustering algorithms techniques are merged in to hierarchical clustering in order to improve cluster quality and also to perform multiple phase clustering. This paper presents a comparative analysis of these two algorithms namely BIRCH and CURE by applying Weka 3.6.9 data mining tool on Iris Plant dataset.