GSOC-2016 Project : Clustering of search results

[Richhiey Thomas]

On Sat, Mar 12, 2016 at 11:38 PM, James Aylett <james-xapian at tartarus.org>
wrote:

> On Sat, Mar 12, 2016 at 04:27:55PM +0530, Richhiey Thomas wrote:
>
> Hi, Richhiey. Thanks for putting this together ahead of the formal
> start of applications, and sharing it with us -- and it's really not
> too long! Project proposals for something that will last the summer
> are naturally going to be long and get into a lot of detail, so don't
> shy away from that.
>
> Thanks for the quick reply James!
I'll try to answer all your questions in the best way possible by me and
send across a better version of this proposal as soon as possible, fitting
the Xapian GSOC application template properly (As you mentioned about the
project motivation part) :)


> I'd like to ask a few questions about the paper, which hopefully you
> can answer, because I'm a little confused by it in places.
>
> For instance, in equation (3) it looks to me like a measure with two
> free variables (i and j) is being defined in terms of two free
> variables (j and m) and one bound one (i). Is the summation actually
> supposed to be over m? (Then within the summation x_i is a dw
> dimensional vector, as is c_j, ie they're both feature vectors, one
> representing a single item and one representing a cluster centroid.)
>
> I may be misreading something fundamentally here, because aspects of
> the combined algorithm just don't make sense to me. It doesn't help my
> understanding that the paper seems to use overlapping references for
> the particle positions and clusters (eg in (3), (4) and I think (5)),
> and the cluster centres that are being used as particle data (at the
> bottom of p207). I think rewriting it with different variables might
> make this a lot clearer. (That may be the main source of my confusion,
> in fact!)
>
The way the paper has been written I guess is the main source of your
confusion. Let me provide a paper that explains this same concept in a way
that is easier to understand. I was confused by eq (3) that you mentioned
too. Here it is :
http://www.sau.ac.in/~vivek/softcomp/clustering%20PSO+K-means.pdf
The whole point of this approach is to use global search of PSO and fast
convergence of K-means together.
So we take every particle and assign it k randomly initialized cluster
centroids. Hence, every particle can be seen as X={C1,C2,..,CK}. Now for
each particle, in every iteration we have to calculate a fitness value for
which we use the equation that you mentioned above that tricked you :)
For every particle, we consider the cluster centroids that we have assigned
to that particle and assign all other documents to the closest of these
cluster centroids. Through this, we can obtain a fitness value which is
calculated by finding the summation of distance between all the documents
and all cluster centroids and taking its reciprocal or making some other
fitness function that we can define to suit or needs better. Using this
fitness function, the next location can be found for the particle.
The combined algorithm uses overlapping references such as cluster
centroids as particle data because we are using PSO to optimize this. Every
particle can be thought of as a candidate solution for the initial
centroids and PSO, being a method used to optimize the candidate solutions
iteratively by moving particles around the search space, helps in finding a
good initial centroid configuration. By the end of the maximum number of
iterations, we arrive at either a sub-optimal or optimal solution of
initial centroids which can then be fed as seed to the K-means algorithm to
provide better clustering results.
I don't know whether I have adequately answered your questions so you can
fire me if I've gone wrong or not been clear anywhere!

>
> As I understand it, though, there are still some parameters to this
> algorithm: the maximum iterations, the inertia weight and two
> 'gravity' constants for the velocity calculation in (1), and K. Is
> that correct? Is there anything we can do to predict a useful value
> for any of them?

I dont know of any ways to predict an optimum value for any of these
parameters though I'm sure there will be some mathematical way to do so.
But I think since we know how each of these parameters would affect the
clustering, we can find the correct values that work well when we test this
algorithm against the kinds of datasets this algorithm will deal with later
on.

> You mean that you're using the term lists of each document to generate
> the particle's position vector in a term-based feature space? (The
> initial centroids for K means and the particle cluster centers are all
> randomly initialised, correct?)
> (That's what I'm expecting, I just want to be sure I'm understanding
> both the paper and what you've written well enough!)

Yes thats exactly what I meant!
  Project Timeline:

> > April 22 - May 22 (Before coding period starts) : Finish any more
> research
> > required by talking to the mentors and any other resources. Have a clear
> > idea of how to go about the project before the coding period
>
> It would be good to identify if there is specific work required during
> this period. What further research do you think is required? Is there
> anything else you need beyond the cited paper in order to implement
> their algorithm?

During this period, the essential goal would be to finalize the particulars
that we will need in the project, For example, finalizing the fitness
measure that will be used for PSO, deciding values for the parameters that
we need for the mixed algorithm or finding a way to predict the optimum
value of each of those parameters if possible, and any other detail that
may be of concern before starting to implement the project

> > May 23 - June 6 (Official coding period starts now) :
> > Define all the classes and functions that will be necessary for the
> > implementation of the project
>
> Why is this going to take two weeks? It sounds from your discussion as
> if you have a good grasp of how to map this into classes
> already. Remember that we really recommend having timeline sections
> of no more than a week. The more you force yourself to break it down,
> the more planning you have to do up front, which generally results in
> a smoother project!
>
Actually James, the problem is I will be having my university exams in this
period and hence, the amount of time I will be able to devote to the
project will be kind of restricted during these two weeks. That's the only
reason I let this part go slow. It still shouldn't take that much time, but
just incase it does, the project wont lag behind.

>
> > June 7 - June 20 :
> > Implement and test the first phase which is a part dealing with the PSO
> > algorithm
>
> Is there a way you can break this into smaller pieces? For instance,
> are there datasets for PSO out there which you could use to build test
> cases, ahead of implementing the algorithm itself? If not, you'll have
> to generate your own, calculating the outcome for some data sets by
> some other method. (You should be able to use a different
> implementation to calculate the test data, because most
> implementations won't have license conditions which restrict the
> subsequent distribution of output of the implementation.)
>
> With step 6 of the algorithm, it looks to me like the K-means part
> that is interwoven with PSO can affect the PSO part, by modifying
> pgd. Does it make sense to tackle things in this way (PSO then
> K-means), or would it make more sense to break it down as steps of the
> algorithm, with each calculation tested separately? (It's probably
> easier to write the tests that way.)
>
> I think the way you suggested is a much better way to look at things
because the two algorithms are tightly coupled and cannot be implemented
independently and tested the way I thought about it.
To break things down further, we can look at things like this
June 7-June 10
Create a class to represent particles and write code to initialize each
particle in the dataset with cluster-centroids
June 11 - June 16:
Implement code to find the fitness value for each particle and finding the
new location of the particle.
June 17-June 20:
Test the code against any dataset which contains documents so that we can
see how the code will be finding the initial centroids and so that we can
tweak the initial parameters to get good performance.

> June 21 - June 27 :
> > Making further changes to the code to improve functionality, exception
> > handling and bug removal
>
> Exception handling is really something you should do as you go
> along. I'm also not clear what 'improve functionality' means in this
> context. Are you concerned that the previous two weeks won't be enough
> time to complete the PSO algorithm? If so, you definitely want to
> break it down into smaller pieces; three weeks is generally too long
> for a single stretch of work (because it's difficult to tell two weeks
> in if it's going well or not).
>
Improve functionality was just meant to say that incase there were portions
that werent implemented completely during the above phase, it can be
completed now. If not, this period can be completely devoted to writing
test cases, or taking care of exception handling and bug removal, to make
sure the code written is in good shape,

>
> > June 28 - July 10 :
> > Implement and test the second phase which deals with the K-means being
> > actually used for clustering.
>
> Again, hopefully you can break this down further.
>
Similar to what I did above, we will just go ahead with implementing the
second phase of the algorithm which now includes incorporating the K-means
algorithm with the seed cluster centroids.
June 28 - July 4:
Using cluster centroid vectors from PSO and implement assigning documents
to the closest centroids.

July 4 - July 12:
Reassigning cluster centroids and finishing up whatever is left in the
complete implementation of the algorithtm

>
> > July 11 - July 18:
> > Making further changes to the code to improve functionality, exception
> > handling and bug removal
>
> And again, breaking down the previous block should mean you don't need
> a 'catch up' period. Don't worry if your estimates go up when you
> break something down further (so something that you thought would take
> about two weeks, when broken into smaller pieces, might look like
> three or even four weeks).
>
I think this period will be essential for writing some test cases,
concentrating on making sure no bugs creep in and on keeping the code
indented and clean and exception free. Also, incase of a delay, this could
be time to level up. By this time, the implementation of the project should
be complete.

>
> > July 17 - July 28 :
> > Implement or use a concrete measure to test the performance of the newly
> > implemented clustering algorithm against the previously implemented one
> in
> > terms of speed and accuracy
>
> Do you have an idea of what measure to use for accuracy? The paper you
> cited uses between and within cluster measures, but there may be other
> techniques. (Also, I don't know if you have access to any
> pre-clustered test data, which would allow you to calculate a
> precision rate.)
>
> It's also going to be important to carefully choose the test data you
> use for this. Is there anything in the TREC collections that would be
> suitable?
>
The new paper that I have referred to uses TREC-5, TREC-6, TREC-7 and so
these datasets can be used. Using entropy and within cluster frequency i
feel would be good indicators of the quality of clustering that we have got

>
> > July 28 - August 1:
> > Find out drawbacks that may come out while testing the proposed solution
> > and tackle it during this time by changing some parts of the code that
> may
> > increase speed or efficiency.
>
> It's good to have a period to incorporate learning from the evaluation
> phase. Is a week enough? (This would be one area where I might
> consider having a longer period, and to be prepared to plan that while
> or just after conducting the evaluation, based on what you learn.)
>
> > August 2 - August 16:
> > Cleaning the written code, improving the documentation or writing
> > documentation afresh, identifying any bugs that may come out and writing
> > test before the final submissions
>
> GSoC has a slightly unfortunately habit of suggesting in their own
> timeline that you work on documentation and tests at the end. We
> strongly recommend that you work on them as you are writing the code
> (with tests in particular, it's much easier to be confident with where
> you are if you have tests as you go, but it's also easier to write
> documentation for something you're working on right now, rather than
> something you coded a month or two previously).
>
> We generally recommend that you include some 'stretch' goals in your
> project, for if everything goes particularly well and you have more
> time at the end. So it would be good to have a think about what else
> could fit within this project. But don't worry if you can't think of
> any; I think with the evaluation phase and then working based on what
> you learn from there that you should have enough to do!
>
> I've tried to breakdown the timeline out here but I'm sure that this is
still not perfect enough. So I'd love it if you could tell me where I am
going wrong and what more detail I could provide so I can think about it
and get back to you.
Also, as of now I'm not able to think of any stretch goals with the project
but I'll let you know if I come up with something worth the while!


> J
>
> --
>   James Aylett, occasional trouble-maker
>   xapian.org
>
>
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