Color Image Clustering Using Combination of Grasshopper Optimization Algorithm and k-means Algorithm

Clustering is one of the most important techniques of data analysis and data mining. Now, by combining algorithms, we try to improve image clustering and use the advantages of each of these algorithms, to cover their disadvantages with each other. One of the most popular of these algorithms is the k-means algorithm. This algorithm is one of the fastest clustering algorithms, but it also has disadvantages. This center-based algorithm is highly dependent on the initial points, and if these points are not selected correctly, we will not have proper clustering. Also, the possibility of local optimization is another shortcoming of this algorithm. We intend to propose a method that combines the k-mean and grasshopper optimization algorithms and makes improvements in image clustering. This hybrid algorithm has both the high speed of the k-means algorithm and the accuracy of the grasshopper optimization algorithm. At the end, the results obtained by this algorithm will be checked with valid datasets to test this combination of algorithms.

Keywords:

Image processing , Image segmentation , Grasshopper Optimization Algorithm , k-means clustering