Determination of the Initialization Number of Clusters inK-means Clustering Application Using Co-OccurrenceStatistics Techniques for Multispectral Satellite Imagery

Abstract

 Abstract—Nowadays, clustering is a popular tool for explo- ratory data analysis, such as K-means and Fuzzy C-mean. Automatic determination of the initialization number of clus- ters in K-means clustering application is often needed in ad- vance as an input parameter to the algorithm. In this paper, a method has been developed to determine the initialization number of clusters in satellite image clustering application using a data mining algorithm based on the co-occurrence matrix technique. The proposed method was tested using data from unknown number of clusters with multispectral satellite image in Thailand. The results from the tests confirm the effectiveness of the proposed method in finding the initializa- tion number of clusters and compared with isodata algorithm. Clustering is a popular tool for data mining and explora- tory data analysis. One of the major problems in cluster analysis is the determination of the number of clusters in unlabeled data, which is a basic input for most clustering algorithms. In this paper, we propose a new easy method for automatically estimating the number of clusters in unlabeled data set. Pixel clustering technique in a color image is a process of unsupervised classification of hundreds thou- sands or millions pixels on the basis of their colors. One of the most popular and fastest clustering techniques is the k- means technique. The results of the k-means technique depend on different factors such as a method of determina- tion of initial cluster centers as shown in Fig. 1. Such sensi- tivity to initialization is an important disadvantage of the k- means technique. In this paper, a method has been devel- oped to determine the initialization number of clusters in satellite image clustering application using a data mining algorithm based on the co-occurrence matrix technique. Therefore, automatic determination of the initialization number of clusters can greatly help with the unsupervised classification of satellite Image.