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Visualizing data helps reveal interesting patterns from large data sets that might not be obvious in some representations. It also aids domain experts in extracting information, generating ideas, and formulating hypotheses from the data. However visualizing big and high dimensional data is challenging due to the human limitation of only being able to visualize up to three dimensions. Moreover, traditional techniques are not capable of processing huge amounts of data, .  Indeed, in the recent years, there has been an explosion of data and it continues to grow by the second. In fact, data generated in the past decade is much larger than all data collected in the past century combined \cite{data2013}. Traditional techniques for data analytics are not capable of analyzing these huge amounts of data due to their processing time that increases exponentially as the number of data increases. To make matters more challenging, this is usually coupled with high dimensionality thus, increasing the complexity of the problem further. For now, no algorithm exists that tackles all the problems of handling big data, although there has been many works that address some specific aspects of it. \cite{xu2016exploring}