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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.
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Interval data clustering using self-organizing maps based on adaptive Mahalanobisdistances Presenter : Min-Cong WuAuthors : Chantal Hajjar, Hani Hamdan2013.NN
Outlines • Motivation • Objectives • Methodology • Experiments • Conclusions • Comments
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 data Ex.temperatures
Methodology -Mahalanobis distance process: R1={[1,2],[3,4],[5,6],[7,8]} RiL=(2,4,6,8). RiU=(1,3,5,7). find Ri’s BMU
Methodology -Computing the prototype vectors Until t=total neighborhood radius Neuron c, Neuron k
Methodology-intSOM_MCDC(m1) totallter↑,σ(t) ↓, because σ init>σfinal
Methodology -intSOM_MDDC(m2)application and training first phase = use common distance 90% iterations second phase = use different distance 10% iterations
Methodology - SOM quality evaluation the topographic error (tpe) measures the degree of topology preservation data classification error (dce) percentage of misclassified data vectors
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-French meteorological real data set 23, 28 and 42 mounted in northeastern regions 24 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