Clustering usually is not the solution where you wish to get a single value.

Clustering will return the set of values where observations happen

approximately. In your case I would suspect four clusters are appropriate,

and they appear to be centered at 17.5, 33.5, 60.5, and 1895. The sample

size is very small so the analysis would be suspect...

To find the cluster centers for an arbitrary number of clusters you minimize

the sum of the distances for each observation to it's cluster center. There

are algorithms out there that we have used in the past. I think we used

K-Means clustering for the project we did, and you should be able to find

papers on it on the net. This may be overkill as the method was not treating

clustering of observations with one value, but rather observations with many

values. We clustered weather data where the centroid was a temperature,

windspeed, humidity, cloudcover object.

If you are trying to code to a set where you don't know the number of

clusters you wish to get out, which is much more common, then you would set

up a loop over the number of clusters (maximum) and find each solution for

cluster count = 2..n. The optimal solution has the lowest distance for any

observation to it's mean.

If in your example, you are really searching for a single value

representative in the data, then I believe the suggestion of removing the top

and bottom extremes. The ammount of data to remove really depends on whether

the data is normally distributed (famous bell curve) or some other

distribution method. From the data provided it would be hard to state that

it was normally distributed.

"Lakesider" wrote:

On 12 Sep., 11:46, JS <jnospams...@gmail.comwrote:
Or use the median, which is 34 in your example.

Thank you. Unfortunately the median is not a good way for me, because

there can be a lot of values out of the "cluster point".