Application of Fuzzy Clustering and DM in Information Extraction of Machine Learning

Abstract—Data mining (DM) uses ordinary methods to discover useful knowledge from a large amount of data, which mines the implicit laws of poor information in the database. Cluster analysis is an important field of DM, which is to classify things in light of certain requirements and rules and plays very important role in mining the useful data. Combining with the fuzzy clustering and using the general mathematical system theory, the fuzzy clustering system model is setup. Fuzzy theory is applied to cluster analysis of DM and how to make use of the fuzzy relationship among samples to analyze the correlation is discussed. The main application steps of fuzzy clustering analysis in DM and the corresponding example are given. Through analysis and discussion, it is concluded that there are a few of different classification about machine learning modes. Comparing with the real records, the results by the fuzzy clustering are in keeping with the investigation in information extraction of machine learning.