An Intelligent Subtype Fuzzy Cluster based Relevant User Data Retrieval Model for Effective Classification

Information retrieval is the process of collecting the data or information from the large volume of data that are available in a local repository or internet according to the user query. Many information retrieval approaches are used by various organizations to facilitate the internet users. Even though, these approaches are consuming more time for the huge data. For reducing the retrieval time, many techniques proposed including clustering techniques for reducing the time. However, they failed to retrieve the more relevant data with less time. For overcoming these drawbacks, this paper proposes an intelligent subtype fuzzy cluster based relevant user data retrieval (ISFCDR) model for effective relevant data retrieval and classification for performing better performance in data recommendation. The proposed model developed by using the existing fuzzy clustering algorithm and classification. Moreover, the existing mutual information based preprocessing also has been done for enhancing the performance of the proposed model. The proposed system is evaluated by conducting various experiments and proved that is better than other existing works in terms of time taken and the content quality.