Abstract—Nowadays, clustering is a popular tool for exploratory data analysis, such as K-means and Fuzzy C-mean. Automatic determination of the initialization number of clusters in K-means clustering application is often needed in advance 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 initialization number of clusters and compared with isodata algorithm.
Index Terms—Determination a number of clusters, number of cluster, K-mean
Kitti Koonsanit and Chuleerat Jaruskulchai are with Kasetsart University, Bangkok, Thailand (e-mail: firstname.lastname@example.org).
Apisit Eiumnoh is with National Center for Genetic Engineering and Biotechnology, Patumthani, Thailand.
Cite: Kitti Koonsanit, Chuleerat Jaruskulchai, and Apisit Eiumnoh, "Determination of the Initialization Number of Clusters in K-means Clustering Application Using Co-Occurrence Statistics Techniques for Multispectral Satellite Imagery," International Journal of Information and Electronics Engineering vol. 2, no. 5, pp. 785-789, 2012.