Clustering SOM Units

On the trained SOM, it is difficult to distinguish subsets because there are still no boundaries between possible clusters. Therefore, it is necessary to subdivide the map into different groups according to the similarity of the weight vectors of the neurons. We can use several different clustering algorithms to divide the trained SOM units into several subgroups. First, the unified distance matrix algorithm (U-matrix developed by Alfred Ultsch) is popular to present overall similarities of SOM units. The U-matrix calculates distances between neighboring map units, and these distances can be visualized to represent clusters using a grayscale display on the map. The matrix is presented as a grayscaled picture based on the calculated values: bright areas with low values depict short distances while dark areas with high values represent long distances to the surrounding neighbors. Consequently, high values of the U-matrix indicate group boundaries, while low values reveal groups themselves.

To determine the number of clusters on SOM units, hierarchical clustering analysis is also commonly used because it can provide hierarchical similarities among SOM units based on linkage distances as criteria. A k-means method may also be applied to the trained SOM.

10 Ways To Fight Off Cancer

10 Ways To Fight Off Cancer

Learning About 10 Ways Fight Off Cancer Can Have Amazing Benefits For Your Life The Best Tips On How To Keep This Killer At Bay Discovering that you or a loved one has cancer can be utterly terrifying. All the same, once you comprehend the causes of cancer and learn how to reverse those causes, you or your loved one may have more than a fighting chance of beating out cancer.

Get My Free Ebook

Post a comment