presenter : min-cong wu authors : chantal hajjar , hani hamdan 2013.nn

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


Outlines

MotivationObjectivesMethodologyExperimentsConclusionsComments


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.


Objectives

• we proposed two methods, Both methods use the Mahalanobis distance to find the best matching unit of an interval data vector.


Methodology - Mahalanobis distance

Input:

Interval dataEx. temperatures



R1={[1,2],[3,4],[5,6],[7,8]}RiL=(2,4,6,8).RiU=(1,3,5,7).

process:find Ri’s BMU


Methodology - Computing the prototype vectors

neighborhood radius Neuron c, Neuron k

Until t=total


Methodology-intSOM_MCDC(m1)

totallter↑, σ(t) ↓, becauseσ init> σfinal


Methodology -intSOM_MDDC(m2)application and training

first phase = use common distance

second phase = use different distance90% iterations

10% iterations


Methodology - SOM quality evaluation

the topographic error (tpe)

data classification error (dce)

measures the degree of topology preservation

percentage of misclassified data vectors


Experiment – Artificial interval data set


Experiment - Clustering results


Experiment - Real temperature interval data set

tpe=4.7

tpe=6.6

tpe=6.6


Experiment - Clustering results and interpretation

17.36

taking the monthly average temperatures


Experiment - Comparison with other methods-Simulated data


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


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.


Comments• Advantages

- a better topology preservation.Applications - self organizing map

presenter : min-cong wu authors : chantal hajjar , hani hamdan 2013.nn

Documents

intervalvalued data

interval data vector

nn interval data clustering

interval dataex

selforganizing maps

neuron kuntil t

common distancesecond

adaptive thats method