Prominent Sampling Techniques Analysis in Machine Learning: Bibliometric Survey and Performance Evaluation

The issue of whether data collection can use an effective sampling strategy presents a challenge to the researcher. Researchers must be familiar with the distinctions in order to choose the most suitable sampling technique or method for the particular study under consideration, given the vast number and variety of sampling techniques and methods that are available. The bibliometric analysis also done for the sampling technique with computer science according to country wise and year wise which provide better ideas for future research. The scope of keyword and citation for the document describe with results obtained using the VosViewer software and data from the Web of Science and Scopus databases unsupervised ML techniques are used to solve the sampling problem. There are different types of ML techniques that are applied to sampling, i.e., K-Mean clustering, DBSCAN, Fuzzy clustering, etc. This research also examines the fundamental ideas of probability sampling, as well as various probability sampling approaches and their strengths and weaknesses.