1. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 1 Visualization of multi-algorithm clustering for better economic decisions - The case of car pricing Presenter : Wu, Jia-Hao Authors : Ran M. Bittmann, Roy Gelbard DSS (2009) 國立雲林科技大學 National Yunlin University of Science and Technology

2. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 22 Outline Motivation Objective Methodology Experiments Conclusion Personal Comments

3. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 3 Motivation Decision makers must analyze diverse algorithms and parameters on the decision-making issues they face. There is no supportive model or tool which enables comparing different result-clusters generated by these algorithms and parameters.

4. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 4 Objective The authors developed a methodology called Multi Algorithms Voting (MAV). The visualization format of MAV just like “Tetris-like”, which enables a cross-algorithm presentation.

5. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 5 Methodology – Multi Algorithms Voting The Tetris-like format is composed of rows, columns and colors.  Each column represents a specific algorithm.  Each line represents a specific sample case.  Each color represents a “Vote”. Algorithms Sample case Vote

6. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 6 Methodology – Meter Squared Vote Error (SVE)  Calculated as the square sum of all the algorithms that did not vote for the chosen classification. H=(7-6) 2 The classification same. The classification different. H=(7-4) 2

7. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 7 Methodology – Meter Distance From Second Best (DFSB)  Calculated as the difference in the number of votes that the best vote. H=(6-1) The classification same. The classification different. H=(4-2)

8. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 8 Experiments The case of car pricing and the cars in the dataset were classified into three price classes. The authors use 14 parameters for each car to perform the clustering.  The car manufacturer.  The car’s engine size.  The number of air bags in the car… Use five algorithms to classification all dataset.  Average Linkage (between Groups)  Average Linkage (within Groups)  Single Linkage  Median Method  Ward Method

9. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 9 Experiments M3, Single Linkage, was unable to match this class correctly. M4, Median method, correctly classified all the cars. Samples 54 and 60 were classified as belonging to the second class by many algorithms.

10. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 10 Experiments Samples 66~71 were classified as belonging to the first price class by most algorithms. The 72 was classified as belonging to price class three, suggesting it is under-priced.

11. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 11 Experiments The price class proved to be the hardest one to classify. M5 is an exception to the rule and proved to be quite effective in classifying cars belonging to this class.

12. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 12 Conclusion Visual presentation of multi-classifications allows the decision maker to identify the right models. The MAV can see the result-clusters of algorithms and evaluate the algorithms. Use the case of cars pricing to identified that are suspected to be overpriced and under-priced.

13. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 13 Comments Advantage  A interesting format to compare the results. Drawback  … Application  Cluster analysis.  Decision support.