Identifying outliers in an unknown distribution I have a sorted sequence of integers, e.g.

1,2,480,1000,1100


representing volumes in some categories. The task is to separate the valid data (high volumes) from the outliers (low volumes).

In the above example I expect following obvious split

1,2   |   480,1000,1100


This is of course trivial, if there is a definition of outliers, such as less than .1% of the total volume, but I'd prefer to have an 
algorithm for calculating this threshold as a result.

I don't know anything about the distribution of the values, except for the outliers have low values and the regular categories have high volumes.

Intuitively, I'd say the approach should pass all possible N-1 splits of the sequence and evaluate some criterion, but I fail to
find a meaningful one (e.g. I distinctly failed comparing the WSS of the sequence with the sum of the WSS of both splits).

I'd appreciate hints to the algorithm or to transformation of this problem in other well known unsupervised problem.
If the high volume and low volume data both appear in non-negligible probability, you may view the problem as a clustering problem with two clusters. Gaussian mixture model or K-means algorithm may help you in this case.