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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