A Discretization Algorithm of Numerical Attributes for Digital Library Evaluation Based on Data Mining Technology

Abstract

We present here a discretization algorithm for numerical attributes of digital collections. In our research data mining technology is imported into digital library evaluation to provide a better decision-making support. But data prediction algorithms work not well based on the traditional discretization method during the data mining process. The reason is that numerical attributes of digital collections are complicated and not in the same scale of distribution distance. We study the characteristic of numerical attributes and put forward a discretization method based on the Z-score idea of mathematical statistics. This algorithm can reflect the dynamic semantic distance for different numerical attributes and significantly enhance the precision rate and recall rate of data prediction algorithms. Furthermore a ‘nonlinear conditional relationship’ among attributes of digital collections is discovered during the study of discretization algorithm and impacts the actual application result of traditional data mining algorithms.