Outlier Removal Clustering through Minimum Spanning Tree
Thangavel Karthikeyan, S. John Peter · 2011
Minimum spanning tree-based clustering algorithm is capable of detecting clusters with irregular boundaries. Detecting outliers using clustering algorithm is a big desire. Outlier detection is an extremely important task in a wide variety of application. In this paper we propose a minimum spanning treebased clustering algorithm for detecting outliers. The algorithm partition the dataset into optimal number of clusters. Outliers are detected in the clusters based on outlyingness factor of each point (objects) in the cluster. The algorithm uses a new cluster validation criterion based on the geometric property of data partition of the data set in order to find the proper number of clusters. The algorithm works in two phases. The first phase of the algorithm creates optimal number of clusters, where as the second phase of the algorithm detect outliers. Keywords: