Intelligent Database Systems Lab Presenter : BEI-YI JIANG Authors : GUENAEL CABANES, YOUNES BENNANI, DOMINIQUE FRESNEAU ELSEVIER Improving the Quality of Self-Organizing Maps by Self-Intersection Avoidance

Intelligent Database Systems Lab Outlines Motivation Objectives Methodology Experiments Conclusions Comments

Intelligent Database Systems Lab 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.

Intelligent Database Systems Lab 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.

Intelligent Database Systems Lab Methodology Prototype enrichment Clustering of prototypes Modeling data distributions Visualization

Intelligent Database Systems Lab Prototype enrichment Methodology-learning data structure Input: The distance matrix Dist(w, x) between the M prototypes w and the N data x. Output: The density Di and the local variability si associated to each prototype wi. The neighborhood values vi,j associated with each pair of prototype wi and wj.

Intelligent Database Systems Lab Principle Methodology-learning data structure −Density modes. It is a measure of the data density surrounding the prototype (local density). −Local variability It can be defined as the average distance between the prototypes and the represented data. −The neighborhood This is a prototype’s neighborhood measure.

Intelligent Database Systems Lab Algorithm Methodology-learning data structure

Intelligent Database Systems Lab Clustering of prototypes Methodology-learning data structure Input: Density values Di. Neighborhood values vi,j. Output: The clusters of prototypes.

Intelligent Database Systems Lab Algorithm Methodology-learning data structure

Intelligent Database Systems Lab Algorithm Methodology-learning data structure

Intelligent Database Systems Lab Methodology-learning data structure

Intelligent Database Systems Lab Presents some interesting qualities Methodology-learning data structure −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.

Intelligent Database Systems Lab Modeling data distributions Density function Methodology-learning data structure

Intelligent Database Systems Lab Algorithm Methodology-learning data structure

Intelligent Database Systems Lab Methodology-A new two-level coclustering algorithm

Intelligent Database Systems Lab Methodology-A new two-level coclustering algorithm

Intelligent Database Systems Lab Experiments

Intelligent Database Systems Lab Experiments

Intelligent Database Systems Lab Experiments

Intelligent Database Systems Lab 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.

Intelligent Database Systems Lab Comments Advantages -Resolve some clustering problems -Obtained results are easy to read and understand -Enhance the data structure Applications - Analyze and visualize biological experimental