A comparative analysis of clustering algorithms for agricultural data

Data mining is emerging research field in agricultural data analysis. There are various data mining techniques such as classification, clustering, prediction, and outlier analysis can be used for the purpose. Clustering is a data mining technique used for discovering groups and identifying interesting distribution in the underlying data. Clustering algorithm used in data mining such as k-means algorithm, density based, k-medoids, hierarchical based and model based latent class analysis. Several optimization methods are proposed in the literature in order to solve clustering limitations, but Swarm Intelligence (SI) has achieved its remarkable position in the concerned area. Particle Swarm Optimization (PSO) is the most popular SI technique which is used in this researcher. In this research a comparisons will be show among k-means algorithm, Hierarchical clustering with centroid Linking and Corelation based feature selection with particle swarm optimization using WEKA data set, UCI data set and agricultural data in order to find out the best clustering algorithm among them in terms of accuracy rate in clustering the data.