A Decision Tree Approach to Cube Construction Patrick Kelly

Data Cube Attributes Designed for quick viewing and decision-making Built to optimize the users time Cube construction is labor intensive Users can only travel along pre-planned path of data analysis Dimensions are designed from pre- existing warehouse tables Reports are structured and ridges like the construction of the cube

Decision Tree Characteristics Decision Tree analysis is a excellent analysis tool used in data exploration Decision Trees are designed to explicitly address the need to identify the relationships between the data’s variables. Decision Tree looks though more relationships than the multidimensional cube. Decision Tree verifies and validates relationships as statistically sound.

Comparison of Multidimensional Cubes and Decision Trees Pg 134 of Decision trees for Business Intelligence and Data Mining Multidimensional CubeDecision Tree Shows tabular views of data as tables with relatively fixed dimensions; dimensions are determined primarily on the basis of business rules Shows tabular views of data within relevant dimensions as determined by computational algorithms and business rules Has database that is pre-built to support anticipated queries Has database that is pre-built to support numerous unanticipated queries Provides quick view retrievalHas lengthy retrieval Tends to limit number of cross-views or relevant factors Has few limitations on the relevant factors Makes it difficult, almost impossible to identify novel results Emphasizes novel results and the identification of important versus unimportant contributions

Construction of Decision Trees Using Data Cube Lixin Fu Decision Trees are not suited for large aggregated data sets Use a Sparse Statistics Tree (SST) to design the OLAP cube. Create Decision Tree using the most current data stored in the cube

Conclusion Decision Trees Decision tree analysis is a good analysis to be done in data exploration Decision Tree analysis is a possible validation test for Cube