Data Mining Model Based on Improved Ant Colony Algorithm

Abstract

Data mining technology is an important means and way of data analysis in the Internet era, and it is a process of extracting potentially useful information from large databases. From the perspective of theory and technology, data mining is a process of discovering the relationship between data and models or between data from a huge database. From the application level, data mining can provide the government and enterprises with valuable and different levels of knowledge, and provide strong technical support for social development. The existing data methods mainly include classification method, association analysis method, cluster analysis method and anomaly detection method, but these methods have some shortcomings. Aiming at the problems of big randomness, sensitivity to parameters and slow convergence speed of existing ant colony clustering algorithm, this paper proposes a data mining model based on improved ant colony algorithm, which will be applied to cluster analysis of experimental data sets. The experimental results show that the improved algorithm has higher accuracy and faster convergence speed, and can effectively realize data information mining.