Hi,
Sorry for late reply. I was playing with REDD
<http://redd.csail.mit.edu/>dataset for Non Intrusive Load Monitoring.
Two issues with k-means:
1. Due to inherent nature of algorithm and random initializations, i
keep ending up with different clusters. These different clusters correspond
to different states of an electrical appliance (ON, OFF, Heater on etc)
2. When number of points is very large then it tends to ignore states
with relatively less number of points
I think with careful manual intervention i can figure our the right cluster
centroids (amongst the ones generated on re-runs). But, in general, it
would be good to have something more specific for 1d.
Jenkins method looks very close to K-Means. Haven't tried it yet, wanted to
know the intuition behind why it could be better than K-Means.
On Tue, Mar 5, 2013 at 8:45 PM, Ronnie Ghose <ronnie.gh...@gmail.com> wrote:
> interesting posts :).
>
> so
> 1) do we want a natural breaks method?
> https://en.wikipedia.org/wiki/Jenks_natural_breaks_optimization
> 2) have you considered looking at the distribution of the variable as they
> suggest? any small-d tends to allow this rather than the usual giant-d
> space.
>
> Do you have any general data set you could release w.r.t. this variable
> nipun? the first question has very clear breaks if you use a histogram alone
>
>
>
>
> On Tue, Mar 5, 2013 at 9:59 AM, nipun batra <nipunredde...@gmail.com>wrote:
>
>> It should. I would have straight away tried it, but read the following 2
>> posts:
>>
>> 1.
>> http://stackoverflow.com/questions/11513484/1d-number-array-clustering
>> 2. http://stats.stackexchange.com/questions/13781/clustering-1d-data
>>
>> Any thoughts?
>>
>> On Tue, Mar 5, 2013 at 8:24 PM, Ronnie Ghose <ronnie.gh...@gmail.com>wrote:
>>
>>> ..........does kmeans not work?
>>>
>>>
>>> On Tue, Mar 5, 2013 at 9:51 AM, nipun batra <nipunredde...@gmail.com>wrote:
>>>
>>>> Hi,
>>>> What clustering technique (with implementation in sklearn) is
>>>> recommended for 1d data?
>>>>
>>>>
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