Abstract
Big data analytics in the field of mobile commerce gathers huge measures of data, yet it doesn't use the information to settle on constant choices. Rather, there is ordinarily a slack between when the data is gathered and when the data is dissected. In short, such data is so substantial and complex that none of the conventional data the executives’ devices can store it or procedure it effectively. The moto of this article is to analyze the big data analytics in mobile commerce field. In m commerce area customer reviews is an important thing to purchase products. Here we mine the high customer reviews based on K means clustering algorithm to cluster the reviews as per the features. The proposed work optimizes the features by using Salp Swarm Algorithm (SSA) to find the efficient features. The performance of the proposed work relates to group the reviews, and ranking the reviews for particular sites based on some products. The result depicted that Amazon and flip kart performs better reviews from customers in mobile commerce sites compared to other shopping sites. The proposed result gives minimum cost, high quality and best brand performs in Amazon platform than others and recognize optimally utilizing the K-means clustering algorithm.
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