1. Intelligent Database Systems Lab Presenter: Jheng, Jian-Jhong Authors: Hansenclever F. Bassani, Aluizio F. R. Araujo 2015 TNNLS Dimension Selective Self-Organizing Maps With Time-Varying Structure for Subspace and Projected Clustering

2. Intelligent Database Systems Lab Outlines Motivation Objectives Related Work Methodology Experiments Conclusions Comments 2

3. Intelligent Database Systems Lab Motivation

4. Intelligent Database Systems Lab 4 Original SOM subspace clustering It was not designed to deal with subspace clustering.

5. Intelligent Database Systems Lab 5 DSSOM The fixed topology of SOM and DSSOM requires deep knowledge of the date to be defined.

6. Intelligent Database Systems Lab 6 SOM-TVS However, certain adaptations to this approach are required for dealing with subspace clustering.

7. Intelligent Database Systems Lab 7 LARFDS SOM Important modifications with respect to DSSOM.

8. Intelligent Database Systems Lab Objectives Important modifications with respect to DSSOM. 8

9. Intelligent Database Systems Lab S UBSPACE AND P ROJECTED C LUSTERING

10. Intelligent Database Systems Lab S UBSPACE C LUSTERING

11. Intelligent Database Systems Lab P ROJECTED C LUSTERING Projected clustering seeks to assign each point to a unique cluster, but clusters may exist in different subspaces. The general approach is to use a special distance function together with a regular clustering algorithm.

12. Intelligent Database Systems Lab Generalized Principal Components Analysis (GPCA) Sparse Subspace Clustering (SSC) Adaptive Resonance Theory (ART) Local adaptive receptive field self-organizing map for image color segmentation (LARFSOM) Related Work 12

13. Intelligent Database Systems Lab GPCA PCA GPCA GPCA is an algebraic geometric method for clustering data not necessarily in independent linear subspaces.

14. Intelligent Database Systems Lab SSC SSC is based on the idea of writing a point (x j ) as a linear or affine combination of neighbor data points. It uses the principle of sparsity to choose any of the remaining data points as a possible neighbor.

15. Intelligent Database Systems Lab Methodology

16. Intelligent Database Systems Lab

22. EXPERIMENTS

23. Intelligent Database Systems Lab

28. Conclusions The behavior of LARFDSSOM was shown to have led to a significant improvement over DSSOM in a number of points. It does not need to know the exact number of clusters. Improvement entails the computational cost. Improvement is the clustering quality. 28

29. Intelligent Database Systems Lab Comments 29