Presenter : Fen-Rou Ciou Authors : M.H. Ghaseminezhad , A. Karami 2011,ASC

Intelligent Database Systems Lab

Presenter : Fen-Rou Ciou

Authors : M.H. Ghaseminezhad, A. Karami

2011,ASC

A novel self-organizing map (SOM) neural network for discrete groups of data

clustering


OutlinesMotivationObjectivesMethodologyExperimentsConclusionsComments


Motivation• However, no algorithm that can automatically

cluster discrete groups of data is presented, and

our simulation results show that the classic SOM

algorithm cannot cluster discrete data correctly.


Objectives• In this paper present a novel SOM-based algorithm

that can automatically cluster discrete groups of data

using an unsupervised method.


Methodology


Methodology – First PhaseInitialize

Find the value

Choose an input vector X

Calculate the distance

Update weights

Set t = t+1Until t = T

Initialize all weights wij

Set Learning iteration number t=0, topological neighborhood d0 , Learning rate ᾳ0 , Total iterations T , Total number of neurons M

t < T


Methodology – Second PhaseInitialize

Calculate “batch”

parameter

Find the value

Choose an input vector X

Calculate the distance

Increase the age

Update weights

If b < Batch

Update weights

Set t = t+1Until t = T

Initialize all weights wij

Set Learning iteration number t=0, topological neighborhood d0 , Learning rate ᾳ0 , Total iterations T , Total number of neurons MBatch0 ,


Methodology – Third PhaseInitial M and set i

= 1Choose

Union also set i

= i + 1If i < M


Methodology


Experiments


Experiments


Conclusions• The novel SOM algorithm does a substantially better

job of clustering discontinues data as a result of its

flexible structure as well as employing the batch

learning method.


Comments• Advantages

• Applications– SOM

Documents

Presenter : Fen-Rou Ciou Authors : M.H. Ghaseminezhad , A. Karami 2011,ASC