Computed Similarity

Computed similarity is a method used to provide a similarity measure between knowledge nodes based on the parameters assigned to each node, the risk associated with each node and the structural placement within the map of each node.

Similarity values are normalised to values between 0 and 100; the lower the value, the closer the similarity. Nodes that are shown as the most similar will have similarity values of less that 10. Dissimilar nodes will have the highest computed values.

Whilst computed similarity does not indicate any conceptual relationship between knowledge elements, the computed similarity may indicate that actions thought to be advantageous for one knowledge area may usefully be considered with respect to the most similar knowledge to that area.

The map contains knowledge that has some relatively tight clusters, found using a similarity computation of parameter values and map structure. Some of these clusters are relatively large with the largest taking 7% of the nodes on the map. There are 23 nodes that are part of clusters with more than two items, 77 nodes that are clustered with only one other node and 4 nodes that have no cluster values computed.