Swarm-based clustering algorithm for efficient web blog and data classification

Abstract

Data classification and the weblog classification have become the most regular approach for people to express themselves. Data classification is another type of problem for classifying a feature set into several feature subsets, and those are further clustered into different classes on the basis of binary or multiclassification. Many problems in science and technology, industry and commercial business and medicine and health care can be treated as classification problems. In recent years, many methods are existing to build a classification model based on many statistical concepts and optimization methods. One major issue of building statistical model will have the principle to provide good accuracy simply when the principal assumptions are correct. The classification decision made on accuracy only justifies the performance of the particular model. Before applying the model to the particular application, it requires good perceptive of data utilized. In order to provide an effective learning algorithm to refine such complexity in handling the data and to minimize output errors and to provide the hands to improve the efficiency of the model, this research article is framed. In this work, a novel algorithm named ‘swarm-based cluster algorithm’ is proposed to complete the feature selection task in order to produce optimized feature-based clusters for effective data and weblogs classification.