Iris Classification using Agglomerative Hierarchical Clustering Approach

Abstract

One of the usages of cluster analysis is to understand unknown data, given the value of several selected features. There are a lot of methods have been proposed data understanding. This paper discusses one technique of data clustering which is called the agglomerative hierarchical clustering algorithm. More specifically, this paper applies that algorithm to understand Iris plant data given the size of the sepal and petal. The system results the Iris flowers groups according to the given threshold value. Moreover, the system produces the analysis of the clusters in order to identify the optimal clusters. The proposed system is implemented as the generalized version of the hierarchical agglomerative clustering algorithm, applied on the Iris data set. But this system can be executed on any data set which would like to cluster the objects by means of that algorithm.