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:
Quote:
On 12 Sep., 11:46, JS <jnospams...@gmail.comwrote:
Quote:
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".
>
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