Research of clustering algorithm based on different data field model

Data field clustering algorithm possesses dynamic characteristics compared with other clustering algorithms. By changing the parameters of the data field model, the results can be dynamically adjusted to meet the target of feature extraction and knowledge discovery in different scales, but the selection and construction of data field model can give rise to different clustering results. This paper presents the different effectiveness of clustering based on various of data field models and its parameters, provides with the scheme to chose the best data field model fitting to the characteristics of the data radiation, and verifies that the best clustering effectiveness can be achieved with the value of radial energy in the golden section. Copyright © 2013 Trans Tech Publications, Switzerland.