Research on Data Mining Technologies for Complicated Attributes Relationship in Digital Library Collections

Publisher:
Natural Sciences Publishing Corporation
Publication Type:
Journal Article
Citation:
Applied Mathematics & Information Sciences, 2014, 8 (3), pp. 1173 - 1178
Issue Date:
2014-05-01
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The authors present the research work on data mining technologies for complicated attributes relationship in digital library collections. Firstly, the work and ideology is introduced as the research background of this paper. Secondly, related preliminary research is introduced. The authors 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.
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