Survival Study of Machine Learning Techniques for Big Data Clustering on Geo-social Networks

Geo-social networks enable users to relate content with geographic locations. The progress of large smart devices comprising of locating components has developed many geo-social networks. Locations are reported by users to offer different position-based services. In addition to, geo-social networks employs huge dataset of gathered user locations for providing users crowd-sourcing location based services. Clustering is designed as an efficient recognition tool to accurately examine geo-social network data. But, the existing clustering techniques failed in grouping geo-social network data in an efficient manner. Our research work is concentrated on different machine learning methods to achieve efficient big data clustering on geo-social network.