Research on data mining technologies for complicated attributes relationship in digital library collections

We present here the research work on data mining technologies for complicated attributes relationship in digital library collections. Firstly our work and ideology is introduced as the research background of this paper. Digital library evaluation is an important topic in information systems domain. We creatively import data mining technologies into it to get an intelligent decision support. But traditional data prediction algorithm didn't work well. This is the problem which would be solved in this paper. Secondly related preliminary research is introduced. We researched on attributes of digital library collections, proposed a parallel discretization algorithm based on z-score theory, and by the discretization algorithm discovered a complicated condition attribute relation among attributes, it is the reason why traditional data prediction algorithm didn't work well. At last a stratified decision tree algorithm for value prediction about digital collection is put forward as the ultimate solution to solve the problem. Stratified attribute concept is imported in this algorithm. It can expand the selection of splitting attribute in decision tree from flat information to stereoscopic information, eliminate the influence of complicated condition attribute relationship, nested use existing decision tree algorithms, and solve the bottleneck of data mining application in digital library evaluation.