Most datasets contain information that is three-dimensional (3D). For example, medical data include multiple values: diagnosis, medical procedures, prescription medicines, tests results, age, gender, and length of stay. The issue with most large datasets is how to analyze and visualize 3D data in a two-dimensional format. Cluster analysis allows the partition or segmentation of 3D data.
The best example that I have found to explain cluster analysis is brain imaging. In the example below, you can three images of the brain. The first image is of the top of the brain, the second of the side, and the third one is from behind. You would use a cluster analysis, to classify what areas of the brain is grey matter, white matter, or fluid. In our brain images, clustering analysis grouped the grey matter as red, the white matter as blue, and the brain fluid as green.
The advantage of cluster analysis is that it allows anyone within a company to make decisions based on a clearer picture of its 3D data.
No comments:
Post a Comment