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1Intelligent Database Systems Lab
N.Y.U.S.T.I. M.
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
2Intelligent Database Systems Lab
N.Y.U.S.T.I. M.
2
Outline
Motivation
Objective
Methodology
Experiments
Conclusion
Personal Comments
3Intelligent Database Systems Lab
N.Y.U.S.T.I. M.
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.
4Intelligent Database Systems Lab
N.Y.U.S.T.I. M.
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.
5Intelligent Database Systems Lab
N.Y.U.S.T.I. M.
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
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N.Y.U.S.T.I. M.
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)2The classification same. The classification
different.H=(7-4)2
7Intelligent Database Systems Lab
N.Y.U.S.T.I. M.
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)
8Intelligent Database Systems Lab
N.Y.U.S.T.I. M.
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
9Intelligent Database Systems Lab
N.Y.U.S.T.I. M.
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.
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N.Y.U.S.T.I. M.
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.
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N.Y.U.S.T.I. M.
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.
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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.
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Comments
Advantage A interesting format to compare the results.
Drawback …
Application Cluster analysis.
Decision support.
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