T - Ceremade

Symbolic Data and Self Organizing Map
Melle Aïcha EL GOLLI
Project AXIS INRIA-Rocquencourt
Lise CEREMADE university Paris IX, Dauphine
[email protected]

6/7 May 2004 Workshop on SDA- Dauphine 2
Complex data
…
Problem of data analysis !!!
Knowledge discovery

6/7 May 2004 Workshop on SDA- Dauphine 3
Data Base Data Base Expert
SObjects 1
SODAS (DB2SO, Dissimilarity, Discrimination, Regression, Visualisation,
Self organizing map (SOM) is neural
network using unsupervised learning:
vector quantization and/or clustering
while preserving the spatial ordering
of the referent vectors (prototype).
Clustering )
SObjects 2 SObjects 3
Results : Symbolic Objects,
clusters, tree, stars ...

6/7 May 2004 Workshop on SDA- Dauphine 4
Summary
¾Introduction classical Self Organizing Map
¾Extension Self organizing Map to dissimilarity data
¾Application in the Web Usage Mining
¾Conclusion

6/7 May 2004 Workshop on SDA- Dauphine 5
Clustering: SOM
1-neighborhood of c
c
2-neighborhood of c
wc={wc1, …wcp}, p is the dimension of input vectors
Neurons organized on a regular low-dimensional map (C, W) described by a graph
C: set of minterconnected neurons having a discrete topology (δrc shortest path on
graph)
W: set of referent vectors (weights)