Presenter : Wu, Jia-Hao Authors : Ran M. Bittmann , Roy Gelbard

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 國立雲林科技大學National Yunlin University of Science and Technology DSS (2009)

2. Outline • Motivation • Objective • Methodology • Experiments • Conclusion • Personal Comments 2

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. 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. Algorithms Sample case Vote 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”.

6. The classification same. The classification different. 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 H=(7-4)2

7. The classification same. The classification different. Methodology – Meter • Distance From Second Best (DFSB) • Calculated as the difference in the number of votes that the best vote. H=(6-1) H=(4-2)

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. 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. 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. 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. 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. Comments • Advantage • A interesting format to compare the results. • Drawback • … • Application • Cluster analysis. • Decision support.