[scikit-learn] Clustering Algorithm based on correlation distance

[Christian Braune]

Using correlation as a similarity measure leads to some problems with
k-means (mainly because the arithmetic mean is not at all an estimator that
can be used with correlation).

If you properly normalized the correlation DBSCAN might be an alternative.
The minpts parameter will still have the same meaning, the eps will state
the maximal allowed difference in correlation (somewhat dubious meaning...)
that points may have when calculating the neighborhoods of points.

But be aware that points belonging to the same cluster (in DBSCAN) might be
completely uncorrelated in the end.

Safi Ullah Marwat <safiullahmarwat at gmail.com> schrieb am Mi., 4. Sep. 2019,
06:42:

> Thank you Mr.Mueller
> Can you share any example sentence? I searched but found this link
> https://stackoverflow.com/questions/24560799/how-to-use-a-precomputed-distance-matrix-in-scikit-kmeans which
> says one cannot supply precomputed distance matric. the one kmean calculate
> precomputed matric that's for speed purpose, but that's too based on
> euclidean distance.
> thanks in advance
>
> On Wed, Sep 4, 2019 at 2:41 AM Andreas Mueller <t3kcit at gmail.com> wrote:
>
>> There are many that allow "metric='precomputed'".
>>
>>
>> On 9/2/19 10:06 AM, Safi Ullah Marwat wrote:
>>
>> Dear List,
>> Is there any clustering algorithm, which is based on correlation
>> coefficient instead of Euclidean/Manhattan distance?
>>
>> Regards
>>
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