Multistage Tree Model for Crime Dataset in Iraq


This research deals with the using of correlation measurement that leads todescribing the degree of relationship between variables, quantities or qualities. Therefore,we implement a simple correlation coefficient and conditional correlation to introduce aregular vine copula, which gives different tree structures. Two methods to select treestructures are introduced. The first one adopts the Partial Correlation Constant (PCC)with constant, while the second method depends on the estimation of summation pathway.The proposed method makes modification on Diβmann’s algorithm to increase thedependency on each level of the tree using rank correlation measurement. Both methodsare adopted to construct the best model with more than three dimensions based on theavailable label crime dataset in Iraq. The selected model is used for selecting the suitabletree model and generating a decision with the low dimensionality of variables.