Home > Academics, Research, Reviews, Tutorials > Cluster Evaluation using Adjusted Rand Index (ARI)

Cluster Evaluation using Adjusted Rand Index (ARI)

Here is the 2 partitions mentioned in the example1 in the tutorial paper “Details of the Adjusted Rand index and Clustering algorithms
Supplement to the paper “An empirical study on Principal Component Analysis for clustering gene expression data” (to appear in Bioinformatics)” pdf

Partition U (ground truth) and V (predicted)

And I think they did in the example is exactly the same as the following

a = |(4,5) ; (7,8)  (7,9) (7,10) (8,9) (8,10) (9,10)| = (2 choose 2) + (4 choose 2) =  7

b=|(1,2) (3,4) (3,5) (6,4) (6,5) (3,6)| = 6

c = |(1,3) (2,4) (2,5) (6,7) … (6,10)| = 7

d = |(1,4)…(1,10) (2,3) (2,6) …(2,10) (3,7) …(3,10) (4,7)…(4,10) (5,7)…(5,10)| = 25

where (i,j) denotes the pair (or edge) between node i and node j. Then they use this a, b, c and d to evaluate Rand index and adjusted Rand index.

Advertisements
  1. No comments yet.
  1. No trackbacks yet.

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s

%d bloggers like this: