Classification by clustering decision tree-like classifier based on adjusted clusters |
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| Authors: | Barak Aviad Gelbard Roy |
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| Affiliation: | 1. Graduate School of Business Administration, Bar-Ilan University, 5290002 Ramat Gan, Israel;2. Nadav Steinberg: Bank of Israel, 91007 Jerusalem, Israel, and Tel-Aviv University, Tel-Aviv, Israel;3. Zvi Wiener: School of Business Administration, The Hebrew University of Jerusalem, 9190501, Jerusalem, Israel;1. Institute of Intelligent Information Processing, Shanxi University, Taiyuan, 030006, Shanxi, China;2. School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, Shanxi, China;1. Laboratory for Machine Tools and Production Engineering WZL at RWTH Aachen University, Campus-Boulevard 30, 52074 Aachen, Germany |
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| Abstract: | 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. |
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