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From Artem Barger <ar...@bargr.net>
Subject Re: [math]: [MATH-1330] - KMeans clustering algorithm, doesn't support clustering of sparse input data.
Date Sat, 30 Apr 2016 20:56:13 GMT
Well, I do not have too much examples in my mind. Actually only one
practical use case, which made me to think that using "double[]" is not
very practical. I was trying to cluster Wikipedia dataset, which is very
sparse (a lot of zero entries) and it looked like huge waste of RAM to
store zeros. Therefore I started to wonder why not to use RealVector
instead, since it has sparse implementation so I will be able to leverage
it. Right now using kmeans++ clustering algorithm provided by common.maths
it's not doable to cluster entire wikipedia dataset or any other huge
datasets.

Another possible alternative is to implement SparseClusterable (inherits
from Clusterable) and the sparse measure which will inherit from
DistanceMeasure and will provide metric computation for such sparse
representation.


Best regards,
                      Artem Barger.

On Sat, Apr 30, 2016 at 11:41 PM, Gilles <gilles@harfang.homelinux.org>
wrote:

> On Mon, 25 Apr 2016 15:52:03 +0300, Artem Barger wrote:
>
>> Hi All,
>>
>> I'd like to provide a solution for [MATH-1330] issue. Before starting I
>> have a concerns regarding the possible design and the actual
>> implementation.
>>
>> Currently all implementations of Clusterer interface expect to receive
>> instance of DistanceMeasure class, which used to compute distance or
>> metric
>> between two points. Switching clustering algorithms to work with Vectors
>> will make this unnecessary, therefore there will be no need to provide
>> DistanceMeasure, since Vector class already provides methods to compute
>> vector norms.
>>
>
> I think that reasons for using "double[]" in the "o.a.c.m.ml.clustering"
> package were:
>  * simple and straightforward (fixed dimension Cartesian coordinates)
>  * not couple it with the "o.a.c.m.linear" package whose "RealVector" is
>    for variable size sequences of elements (and is also, inconsistently,
>    used as a Cartesian vector, and also as column- and row-matrix[1])
>
> It is arguable adapted for a family of problems which the developer
> probably had in mind when taking those design decisions.
>
> It would be interesting to know for which class of problems, the design
> is inappropriate, in order to clarify ideas.
>
> The main drawback of this approach is that we will loose the ability to
>> control which metric to use during clustering, however the only classes
>> which make an implicit use of this parameters are: Clusterer and
>> KmeansPlusPlusClusterer all others assumes EucledianDistance by default.
>>
>
> There is a default indeed, but all "Clusterer" implementations use
> whatever "DistanceMeasure" has been passed to the constructor.
>
> Assuming that "RealVector" knows how to compute the distance means that
> users will have to implement their own subclass of "RealVector" and
> override "getDistance(RealVector)" if they want another distance.
> Alternatively, CM would have to define all these classes.
>
> At first sight, it does not seem the right way to go...
>
> One of the possible approaches is to extend DistanceMeasure interface to be
>> able to compute distance between two vectors? After all it's only sub
>> first
>> vector from the second and compute desired norm on the result.
>>
>
> Seems good (at first sight) but (IMHO) only if we implement a new
> "CartesianVector" class unencumbered with all the cruft of "RealVector".
>
> Another possible solution is to make vector to return it's coordinates,
>> hence it avail us to use DistanceMeasure as is. Personally I do not think
>> this is good approach, since it will make no sense with sparse vectors.
>>
>
> Ruled out indeed if it conflicts with your intended usage.
>
> Last alternative this comes to my mind is to create a set of enums to
>> indicate which vector norm to use to compute distances, also do no think
>> this is very good solution, since sounds too intrusive and might break
>> backward compatibility.
>>
>
> And forward compatibility (clustering code will have to be adapted if
> another distance is added later).
>
> What do you think? Am I missing something? Is there a better possible way
>> to achieve the goal?
>>
>
> As indicated above, a practical example might help visualize the options.
>
>
> Regards,
> Gilles
>
> [1] Cf. https://issues.apache.org/jira/browse/MATH-765
>
>
>> Best regards,
>>                       Artem Barger.
>>
>
>
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