An Data Ming Method Based on AHP and Apriori

As the current personalized recommendation methods of Internet bookstore are limited too much in function, this paper proposes a kind of Internet bookstore data mining method based on “Strategic”, which can provide personalized recommendations that they really want. It helps us to get the weight attribute of type of book by using AHP, the weight attributes spoken on behalf of its owner, and we add it in association rules. The experimental results indicate that the Internet bookstore recommendation method is feasible.

Using "Blackbox" Algorithms Such AS TreeNET and Random Forests for Data-Ming and for Finding Meaningful Patterns, Relationships and Outliers in Complex Ecological Data

The use of machine-learning algorithms capable of rapidly completing intensive computations may be an answer to processing the sheer volumes of highly complex data available to researchers in the field of ecology. In spite of this, the continued use of less effective, simple linear, and highly labor intensive techniques such as stepwise multiple regression continue to be widespread in the ecological community. Herein we describe the use of data-mining algorithms such as TreeNet and Random Forests (Salford Systems), which can rapidly and accurately identify meaningful patterns and relationships in subsets of data that carry various degrees of outliers and uncertainty. We use satellite data from a wintering Golden Eagle as an example application; judged by the consistency of the results, the resultant models are robust, in spite of 30 % faulty presence data. The authors believe that the implications of these findings are potentially far-reaching and that linking computational software with wildlife ecology and conservation management in an interdisciplinary framework cannot only be a powerful tool, but is crucial toward obtaining sustainability.