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Interval data clustering using self-organizing maps based on adaptive Mahalanobis distances. Presenter : Min-Cong Wu Authors : Chantal Hajjar , Hani Hamdan 2013.NN. Outlines. Motivation Objectives Methodology Experiments Conclusions Comments. Motivation. - PowerPoint PPT Presentation
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Intelligent Database Systems Lab
Presenter : MIN-CONG WUAuthors : CHANTAL HAJJAR, HANI HAMDAN2013.NN
Interval data clustering using self-organizing maps based on adaptive Mahalanobis distances
Intelligent Database Systems Lab
Outlines
MotivationObjectivesMethodologyExperimentsConclusionsComments
Intelligent Database Systems Lab
Motivation• In real world applications, data may not be
formatted as single values, may are represented by interval.
• but about self-organizing map for interval-valued data based on adaptive that's method haven't be proposed a lot.
Intelligent Database Systems Lab
Objectives
• we proposed two methods, Both methods use the Mahalanobis distance to find the best matching unit of an interval data vector.
Intelligent Database Systems Lab
Methodology - Mahalanobis distance
Input:
Interval dataEx. temperatures
Intelligent Database Systems Lab
Methodology - Mahalanobis distance
R1={[1,2],[3,4],[5,6],[7,8]}RiL=(2,4,6,8).RiU=(1,3,5,7).
process:find Ri’s BMU
Intelligent Database Systems Lab
Methodology - Mahalanobis distance
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Methodology - Computing the prototype vectors
neighborhood radius Neuron c, Neuron k
Until t=total
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Methodology-intSOM_MCDC(m1)
totallter↑, σ(t) ↓, becauseσ init> σfinal
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Methodology -intSOM_MDDC(m2)application and training
first phase = use common distance
second phase = use different distance90% iterations
10% iterations
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Methodology - SOM quality evaluation
the topographic error (tpe)
data classification error (dce)
measures the degree of topology preservation
percentage of misclassified data vectors
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Experiment – Artificial interval data set
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Experiment - Clustering results
Intelligent Database Systems Lab
Experiment - Clustering results
Intelligent Database Systems Lab
Experiment - Real temperature interval data set
tpe=4.7
tpe=6.6
tpe=6.6
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Experiment - Clustering results and interpretation
17.36
taking the monthly average temperatures
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Experiment - Comparison with other methods-Simulated data
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Experiment - Comparison with other methods-French meteorological real data set
23, 28 and 42 mounted in northeastern regions24 and 23 mounted in western regions
12.71<13.89
Intelligent Database Systems Lab
Conclusions
• we proposed two methods, the second method is more adaptive than the first one because it uses a different distance per cluster in the last iterations of the training algorithm.
Intelligent Database Systems Lab
Comments• Advantages
- a better topology preservation.Applications - self organizing map