Minimum spanning tree based clustering for outlier detection

Abstract

Detecting outliers in database (as unusual objects) using Clustering algorithm is a big desire. Minimum spanning tree based clustering algorithm is capable of detecting clusters with irregular boundaries. In this paper we propose a minimum spanning tree based clustering algorithm for detecting outliers. Outlier detection is an extremely important task in a wide variety of application. The algorithm partition the dataset into optimal number of clusters. Small clusters are then determined and considered as outliers. The rest of the outliers (if any) are then detected in the remaining clusters based on absolute distance between the center points of current cluster to each one of the points in the same cluster. The algorithm uses a new cluster validation criterion based on the geometric property of data partition of the dataset 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. The key feature of our algorithm is it finds noise-free/ error-free clusters for a given dataset without using any input parameters.