Identifying which known groups are the most similar or most dissimilar I have a data set of 5 groups and their associations to different interests. The data is structured as follows with 2000+ interests and 5 known groups:

      Interest     Group1     Group2     Group3     Group4     Group5
01.   Sports         10%        40%        30%       80%         65%
02.   Music          35%        60%        55%       10%         15%
03.   Fashion        80%        10%        75%        5%         25%
 .
 .
 .
1998.  Movies        70%        20%        45%       50%         65%
1999.  Books         50%        70%        35%       65%         95%
2000.  Art           90%        50%        25%       15%         30%


I would like to gain an understanding of what would be the best way to determine which groups are closest to each other and which ones are the most different from each other in terms of interests. 

I am leaning towards cluster analysis, however the structure of my data is throwing me off.

Any suggestions would be greatly appreciated.
You only have five groups, so full blown clustering is probably not a good idea here, but looking at similarity scores between the group vectors may be insightful. An easy one to try at first would be cosine similarity, which essentially measures the angle between each of your group vectors:

similarity = $cos(\theta)$ = $\frac{\mathbf{A} \cdot \mathbf{B}}{||\mathbf{A}||_2 ||\mathbf{B}||_2}$

where $\mathbf{A} \cdot \mathbf{B}$ gives the dot product of two vectors, and $||\mathbf{A}||_2$ is the magnitude of a vector $\mathbf{A}$.

All of your vector values are positive, so the result will be in the range $(0, 1)$. The closer this value is to $1$, the more similar the vectors. $0$ means they are completely decorrelated (vectors are orthogonal). You can compare the cosine similarities of each pair of groups to work out which ones are most similar/dissimilar.