  The hospitality industry is one of the data-rich industries that receives
huge Volumes of data streaming at high Velocity with considerably Variety,
Veracity, and Variability. These properties make the data analysis in the
hospitality industry a big data problem. Meeting the customers' expectations is
a key factor in the hospitality industry to grasp the customers' loyalty. To
achieve this goal, marketing professionals in this industry actively look for
ways to utilize their data in the best possible manner and advance their data
analytic solutions, such as identifying a unique market segmentation clustering
and developing a recommendation system. In this paper, we present a
comprehensive literature review of existing big data clustering algorithms and
their advantages and disadvantages for various use cases. We implement the
existing big data clustering algorithms and provide a quantitative comparison
of the performance of different clustering algorithms for different scenarios.
We also present our insights and recommendations regarding the suitability of
different big data clustering algorithms for different use cases. These
recommendations will be helpful for hoteliers in selecting the appropriate
market segmentation clustering algorithm for different clustering datasets to
improve the customer experience and maximize the hotel revenue.
