A review on density-based clustering algorithms for big data analysis

The rapid advances in internet speed have enabled data on a global scale to be transmitted at very high speeds. In the present era, the spotlight has remained unwavering on big data applications because of the humungous volume of data generation and storage that has occurred during the last couple of years. Everyday a huge amount of data is being generated continuously as data streams from different real world applications such as banking, agriculture, finance, stock management, and healthcare. Data streams are evolving over time and the amount of data is unbounded. The main problem associated with these data streams are their management and storage, analysis and retrieval. This paper presents a survey of different density-based clustering techniques for big data analysis, providing a comprehensive comparison among the different proposed techniques.