Abstract
Clustering analysis has been an emerging research issue in data mining due to its variety of applications. In recently, mathematical algorithm supported automatic segmentation system plays an important role in clustering of images. The fuzzy c-means clustering is a method of cluster analysis which aims to partition n data points into k-clusters. The conventional FCM-based algorithm considers no spatial content information, which means it sensitive to noise. Unsupervised techniques need to be employed, which can be based on minimal spanning tree generated by comparing spatial neighbourhood information, the MST based clustering algorithms have been widely used due to their ability to detect clusters with irregular boundaries. We propose an automatic fuzzy c-means initialization algorithm based on Canberra distance minimal spanning tree for the purpose of segmentation of medical images, where vertices and edges are labelled with multi-dimensional vectors. A Canberra distance measure based, construct the minimal spanning tree clustering algorithm. An efficient method for calculating membership and updating prototypes by minimizing the new objective function of Gaussian based fuzzy c-means. 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 cluster at each level. In this algorithm to apply medical images to reduce the inhomogeneity and allow the labelling of a pixel to be influenced by the labels in its immediate neighbourhood and reduces the time complexity and better clustering results than the existing traditional minimal spanning tree algorithm. The performance of proposed algorithm has been shown with random data set, partition coefficient and validation function are used to evaluate the validity of clustering and then new cluster separation approach to optimal number of clustering. Also this paper compares the results of proposed method with the results of existing basic fuzzy c-means.
Highlights
Clustering is one of the important tools for data analysis
Since similarity/ distance measure is the key requirement for any clustering algorithm, In this paper, we have proposed a new weighted distance measure based on weighted Euclidean norm to calculate the distance between the vertices and the algorithm is initialized by a given kernel function using minimal spanning tree based FCM algorithm, which helps to speed up the convergence of the algorithm
Our algorithm finds the center of each clusters, which will be useful in many applications
Summary
Clustering is one of the important tools for data analysis. It can divide an unlabeled dataset into several subsets according to some criteria to ensure similar samples to be in the same subset and dissimilar samples to be different subset. A recent development is to use kernel method to construct the kernel versions of FCM algorithm, KFCM for clustering the incomplete data and medical image segmentation was proposed [12,13].
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