KMeans and Canopies

[Mark <st...@gmail.com>]

Should canopy generation and KMeans clustering typically use the same 
distance calculation or is possible to mix and match? Any reason why 
some would mix?

Thanks

Re: KMeans and Canopies

[Mark <st...@gmail.com>]

What if one was to use Tanimoto distance measure with KMeans.. would the 
same reasoning apply?

On 6/26/11 1:54 PM, Christoph Brücke wrote:
> Hi Mark,
>
> you typically choose a somewhat cheaper distance metric for the canopy clustering, if used as a preprocessing step for KMeans. A simple example would be Manhattan distance (d = |x1 - x2| + |y1 - x2|) for Canopy clustering and Squared Euclidean distance [d = sqrt( (x1 - x2) ^2 + (y1 - y2) ^ 2) )] for KMeans. This way you got a cheap approximation for your initial cluster centers.
> I hope this was helpful.
>
> Regard,
> Christoph
>
>
> Am 26.06.2011 um 21:29 schrieb Mark:
>
>> Should canopy generation and KMeans clustering typically use the same distance calculation or is possible to mix and match? Any reason why some would mix?
>>
>> Thanks
>>
> Christoph Brücke
> christoph.bruecke@campus.tu-berlin.de
>
>
>

Re: KMeans and Canopies

[Christoph Brücke <ch...@campus.tu-berlin.de>]

Hi Mark,

you typically choose a somewhat cheaper distance metric for the canopy clustering, if used as a preprocessing step for KMeans. A simple example would be Manhattan distance (d = |x1 - x2| + |y1 - x2|) for Canopy clustering and Squared Euclidean distance [d = sqrt( (x1 - x2) ^2 + (y1 - y2) ^ 2) )] for KMeans. This way you got a cheap approximation for your initial cluster centers.
I hope this was helpful.

Regard,
Christoph


Am 26.06.2011 um 21:29 schrieb Mark:

> Should canopy generation and KMeans clustering typically use the same distance calculation or is possible to mix and match? Any reason why some would mix?
> 
> Thanks
> 

Christoph Brücke
christoph.bruecke@campus.tu-berlin.de