Data Mining is considered as a power tool to discover knowledge such as in form of association rule that is useful in business domains for planning,diagnosis,CRM etc.Yet,there are two drawbacks in conventional mining techniques.Since most of the techniques performs flat-mining based on priori-defined patterns in the data warehouse as a whole,so a fully re-scan must be done whenever new attributes are added,i.e.multi-dimensional expansion.On the other hand,an association rule may be true on a certain granularity but fail on a larger one.Most importantly,this kind of works are facing the challenge,i.e.how reliable or how precise the results are fit to the real-world knowledge.The tools and techniques for data mining may need improvements or new approaches to address this change.Aiming at resolving the problems,this paper formulates the mining process as a combination of searching for all patterns;and matching with a user-given validity of rules to find.This paper argues for applying a forest structure consisting of Concept Taxonomies,i.e.Hierarchies,to represent the searching space and the pattern of concepts from individual hierarchies.In order to verify and validate that the mining algorithm proposed in this paper,a set of metrics were defined based on Shannons Entropy Function.Last but not least,the performance of the DM approach,including efficiency,scalability,effectiveness etc.,was tested by means of the metrics defined.The whole process of the development and experimental measurement of the multidimensional mining approach was discussed in this paper.