Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology U*F clustering : a new performant “ clustering-mining ” method based on segmentation of Self-Organizing Maps Presenter : Shu-Ya Li Authors : Fabien Moutarde, Alfred Ultsch WSOM 2005, Paris

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 2 Outline Motivation Objective Methodology  U*F clustering Experiments and Results Conclusion Personal Comments

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 3 Motivation Standard clustering algorithms (K-means, single-linkage and Ward) performs very bad on at least one kind of dataset.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 4 Objectives We propose U*F clustering method which shows consistently good clustering results. U*F clustering method based on automated “flood-fill segmentation” of U*-matrix of SOM after training. Flood-Fill Algorithm

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 5 Methodology – U*F clustering method U-matrix  Flood-fill segmentation of a U-matrix

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 6 Methodology – U*F clustering method U*-matrix  combines the distance-based U-matrix and a density-based P-matrix U*F clustering cluster boundary

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 7 Experiments Datasets and U*F clustering results U*-matrix U-matrix

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 8 Experiments Comparison with other clustering algorithms shows consistently good clustering results

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 9 Conclusion U*F clustering method shows consistently good clustering results. U*F clustering method has the following advantages:  When the categorization is not perfect, examples are left “isolated” rather being attributed to the wrong cluster;  No a priori hypothesis for the number of clusters is required;  The global computation cost is the computation of the U*-matrix, not SOM units.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 10 Personal Comments Advantage  U*F method are good over a wide range of critical dataset types. Drawback  U*-matrix makes it not very well suited for datasets with at least one discrete- valued component.  For several datasets, U*F appears to mistakenly leave a significant proportion of the examples isolated in none of the clusters. Application  Clustering