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

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.

Parameter-Free K-Means Clustering Algorithm for Satellite Imagery Application

“…Hence, the authors define a definition for a co-occurrence matrix by mans of inclusion of the transition of the gray-scale value between the current pixel and the adjacent pixel into the co-occurrence matrix presented in paper [1].…”

“…The definition of a co-occurrence matrix [1][17] [18] presented in this paper is based on the concept that the adjacent pixels should affect a region of clusters. Hence, the authors define a definition for a co-occurrence matrix by mans of inclusion of the transition of the gray-scale value between the current pixel and the adjacent pixel into the co-occurrence matrix presented in paper [1].…”

“…[1] Maxima technique [19] [20], known collectively as extrema, are the largest value (maximum). The input data of maxima function requires a row or column vector with realvalued elements from the diagonal matrix with a minimum length of three.…”

“…Our previous paper regarding the initialization number of clusters in K-means clustering application with a co-occurrence matrix technique has been published. [1] Although our previous approach regarding the number of cluster was discovered, but it was limited to count a number of peaks in occurrence matrix as the number of clusters by manual counting. The best of our previous approach need to automatically find and count a number of peaks in occurrence matrix.…”

A Simple Anomaly Detection for Spectral Imagery Using Co-occurrence Statistics Techniques

“…Our definition of a co-occurrence matrix [15], [16], [17], [14] is based on the idea that the neighboring pixels should affect region of clusters. Hence, we define a definition for a co-occurrence matrix by including the transition of the grayscale value between the current pixel and adjacent pixel into our co-occurrence matrix illustrated in Fig.…”