Attribute Selection Algorithm with Clustering based Optimization Approach based on Mean and Similarity Distance

With hundreds or thousands of attributes in high-dimensional data, the computational workload is challenging. Attributes that have no meaningful influence on class predictions throughout classification process increase computing load. This article's goal to use attribute selection reduce size which will lessen Considering selected subsets cover all attributes. As a result, there are two stages process: filtering out superfluous information and settling single stand for group similar but otherwise meaningless characteristics. Numerous studies selection, including backward forward been undertaken. experiment accuracy categorization result recommend k-means based PSO clustering-based selection. It likely related present same cluster while irrelevant not identified any clusters. Datasets Credit Approval, Ionosphere, Annealing, Madelon, Isolet, Multiple employed alongside other datasets. Both databases include label each data point. Our test demonstrates using clustering may be done offer subset characteristics doing so produces outcomes more accurate than 80%.