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Recently, the world is experiencing generating the huge amount of data in different domains. Data mining and Data analytics and are the practices used for analyzing data and extracting hidden knowledge. One of the major data mining methods which is used to analysis of data is data clustering. Data clustering ease the extract information from each cluster separately. There are many algorithms used to perform the clustering. One of the most famous algorithms which is used for clustering for more than half a century is k-means. By many optimization and enhancement, K-means still considers as the most popular clustering algorithm which is still being used in various domains. This research attempts to conduct a Systematic literature Review (SLR) to collect, classify, and analyze the primary studies about the different version of k-means clustering algorithm. This SLR gives a means of finding, appraising, and interpreting existing researches pertinent to the topic. By narrowing down the crucial sections of debate, we are hoping to establish a foundation for upcoming researches.

Big Data encompasses huge amounts of raw material which influence multitude of research fields as well as different industries performance such as business, marketing, social network analysis, educational systems, healthcare, IoT, meteorology, fraud detection. It aimed to uncover hidden trends and has prompted a development from a model-driven perspective to a data-driven approach. Among numerous properties of Big Data, datasets of Big Data are identified primary as 3Vs attributes which have high variety, velocity and volume. These provide an invaluable insight and assist in making precise decisions. Analyzing this information and outlining the outcome into helpful data is the method for extricating an incentive from these enormous volumes of datasets. Nevertheless, Big Data containing unique features that cannot be handled and processed using the conventional methods. This has presented a significant challenge to the industry. This research paper presents a general outline of the characteristics of Big Data as well as expounds on the present challenges and limitations in this area. It further discusses the future scope in particular the future direction for Big Data research.