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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

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

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Intelligent Database Systems Lab Motivation

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

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

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

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

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

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Intelligent Database Systems Lab S UBSPACE AND P ROJECTED C LUSTERING

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Intelligent Database Systems Lab S UBSPACE C LUSTERING

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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.

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

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

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

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Intelligent Database Systems Lab Methodology

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Intelligent Database Systems Lab

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EXPERIMENTS

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Intelligent Database Systems Lab

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Conclusions The behavior of LARFDSSOM was shown to have led to a signicant 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

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Intelligent Database Systems Lab Comments 29