[mlpack] Comparative analysis of novel clustering algorithms

[Heet Sankesara]

Hello community, Myself Heet sankesara. I am a machine learning
practitioner. I've been doing it for a year. I am currently pursuing my
BTech in CSE from IIIT Vadodara. I am doing research on  Markov Logic
Network. Due to this, I have to leave python and start working with cpp.
I've been learning mlkit for a few weeks now and have a fair idea of the
underlying structure.
 In GSoC ideas list, there is a clustering method Quantum clustering. I
want to work on a few novel clustering algorithms like *sampling clustering
<https://arxiv.org/pdf/1806.08245.pdf>*, *Deep Clustering for Unsupervised
Learning of Visual Features <https://arxiv.org/pdf/1807.05520.pdf>*, Learning
Neural Models for End-to-End Clustering
<https://arxiv.org/pdf/1807.04001.pdf>, *GMM **clustering, *and *Quantum
clustering *etc and do the comparative analysis of them. This comparative
analysis will be aimed at knowing the strength and weakness of each
algorithm and what kind of data is good for which algorithm. Please
consider this idea for GSoC 2K19. I am happy to talk further and discuss
possible algorithms which can be implemented in the upcoming summer.
With Regards,
Heet Sankesara
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