1. Clustering

Clustering procedures aim at yielding a data description in terms of clusters or groups of data points that possess strong internal similarities. For the typhoon image collection, we expect that clustering procedures may produce the intuitive summarization of typhoon cloud patterns that can be used as the catalog of typhoon images. If we can find a set of clusters that represent typical patterns of the typhoon, we can categorize complex cloud patterns into several representative patterns, thereby characterize them with a set of basic patterns. The Dvorak method, the standard typhoon analysis method in the meteorology community, did this task manually and selected typical cloud patterns embodied from the long experience of analysts. In contrast, we do this task automatically.

K-means clustering

K-means clustering The basic non-hierarchical clustering procedure is the K-means clustering. Those images can be considered as representative patterns, and many types of shape are visualized together on a two dimensional space. Hence it is a concise visualization, but it is still not an intuitive visualization for humans due to the lack of ordering between clusters.

Self-Organizing Map

SOM clustering (10x10) SOM clustering (50x50) Self-Organizing Map (SOM) summarizes high dimensional data vectors with a set of reference vectors having a topological organization on a (usually) two-dimensional lattice. They give an improved visualization with apparent spatial ordering. These clustering methods can thus visualize the high dimensional feature space of typhoon cloud patterns in a ``birds-eye-view'' representation, which is effective for understanding the overall distribution at a glance. Thus the ordering of typhoon cloud patterns attained by the SOM gives a unique insight into the nature of typhoon cloud patterns.