Improving the Quality of Self-Organizing Maps by Self-Intersection Avoidance

Intelligent Database Systems Lab

Presenter : BEI-YI JIANG

Authors : GUENAEL CABANES , YOUNES BENNANI , DOMINIQUE

FRESNEAU

2012. ELSEVIER

Improving the Quality of Self-Organizing Maps by Self-Intersection Avoidance


OutlinesMotivationObjectivesMethodologyExperimentsConclusionsComments


Motivation

• The exponential growth of data generates terabytes of

very large databases.• The growing number of data dimensions and

data objects presents tremendous challenges for

effective data analysis and data exploration methods and

tools.


Objectives

• Develop a method of describing data from enriched and segmented prototypes using a topological clustering algorithm.• Provide data visualizations via maps and graphs, to

provide a comprehensive exploration of the data structure.


Methodology

Prototype enrichmen

t

Clustering

of prototypes

Modeling data

distributions

Visualization


• Prototype enrichment

Methodology-learning data structure

Input: The distance matrix Dist(w, x) between the M prototypes w andthe N data x.

Output: The density Di and the local variability si associated to eachprototype wi.The neighborhood values vi,j associated with each pair ofprototype wi and wj.


• Principle


− Density modes.It is a measure of the data density surroundingthe prototype (local density).

− Local variabilityIt can be defined as the average distance between the prototypes and the represented data.

− The neighborhoodThis is a prototype’s neighborhood measure.


• Algorithm



• Clustering of prototypes


Input: Density values Di. Neighborhood values vi,j.

Output: The clusters of prototypes.


• Algorithm



• Algorithm





• Presents some interesting qualities


− The number of cluster is automatically detected by the algorithm.− No linearly separable clusters and non hyper-spherical clusters can be detected.− The algorithm can deal with noise (i.e. touching clusters) by using density estimation.


• Modeling data distributions• Density function



• Algorithm



Methodology-A new two-level coclustering algorithm


Methodology-A new two-level coclustering algorithm


Experiments


Experiments


Experiments


Conclusions

• Propose a new data structure modeling method, based on the learning of prototypes.

• Propose a new coclustering algorithm to solve different kind of problems. The results are easy to read and understand, and are perfectly compatible with biologists knowledge.

• A method of visualization able to enhance the data structure within and between groups.


Comments• Advantages

-Resolve some clustering problems-Obtained results are easy to read and understand-Enhance the data structure

• Applications- Analyze and visualize biological experimental

Documents

Improving the Quality of Self-Organizing Maps by Self-Intersection Avoidance