What is the best way to cluster this kind of data? I have data that looks like this:

The chart on the left is the trend and the smaller chart on the right is the box plot showing the distribution of means. Each color is the output of a particular tool. I dont need to do an ANOVA and Tukey tests on it because it can easily be seen that there are statistically significant differences between the tools.
However, I would like to group/cluster the tools based on their mean.
For example, visually speaking, one can separate all the tools into 4 groups. 4 tools with a mean around 32, 12 tools with a mean around 30, 5 tools whose variance is quite high, and whose mean don't really fall into a particular group and the rest whose mean is around 30.
Update:
I have used the k-means clustering technique and the Hierarchical Agglomeration techniques. However, I have to specify the number of clusters in advance. Is there any clustering technique where I don't have to specify the number of clusters a-priori?
In hierarchical clustering, both agglomerative and divisive, you do not have to pre-specify the number of clusters. You can create all possible clusters and then select the number cluster to use at the end.