In this paper, a new method is proposed for solving the data clustering problem using Cat Swarm Optimization (CSO) algorithm based on chaotic behavior. The problem of data clustering is an important section in the field of the data mining, which has always been noted by researchers and experts in data mining for its numerous applications in solving real-world problems. The CSO algorithm is one of the latest meta-heuristic algorithms, which has a simple structure and it is easy to implement. The purpose of Chaos embedded Cat Swarm Optimization (CCSO) algorithm is to replace random values by chaotic ones to offer a stable algorithm that can allow for reaching the global optima to a large extent and improve the algorithm’s convergence speed. The proposed algorithm has been compared to other heuristic algorithms on standard data sets from UCI repository, and the experimental results demonstrate that the proposed algorithm yields high performance for solving the data clustering problem.Keywords: Data clustering, K-means, Cat Swarm Optimization, Chaos theory. Manuscript profile

Digital image processing in recent decades has made considerable progress in theoretical and practical aspects. Nowadays, machine vision techniques have important application in the field of agriculture. One of these applications is detection of different varieties of rice from the bulk sample of rice image. These techniques also have high speed, accuracy and reliability. Texture feature selection is one of the important characteristics used in pattern recognition. The better feature selection of a feature set usually results in better performance in a classification problem. In This work we try to extract features by using co_occurrence matrix and select the best feature set for classification of rice varieties based on image of bulk samples using hybrid algorithm which is called "fuzzy_ imperialist competition” and then classify the best features using support vector machine(SVM). Results of the proposed method showed, the classification accuracy is improved to 96/79%. The feature set which is selected by the fuzzy-Ica provides the better classification performance compared to that obtained by Imperialist competition algorithm. Manuscript profile