Classification by clustering decision tree-like classifier based on adjusted clusters

Currently cluster analysis techniques are used mainly to aggregate objects into groups according to similarity measures. Whether the number of groups is pre-defined (supervised clustering) or not (unsupervised clustering), clustering techniques do not provide decision rules or a decision tree for the associations that are implemented. The current study proposes and evaluates a new technique to define decision tree based on cluster analysis. The proposed model was applied and tested on two large datasets of real life HR classification problems. The results of the model were compared to results obtained by conventional decision trees. It was found that the decision rules obtained by the model are at least as good as those obtained by conventional decision trees. In some cases the model yields better results than decision trees. In addition, a new measure is developed to help fine-tune the clustering model to achieve better and more accurate results.

Research highlights
► In real life problems Decision Trees do not always provide meaningful decision rules.
► Association rules were replaced with similarity measures as used in clustering.
► A new measure is used to ensure significant size and response rate in all classes.
► Additional measure is used to determine the appropriate weight of each attribute.
► The composed classifier achieves better results than conventional decision trees.