Survey of unsupervised machine learning algorithms on precision agricultural data

摘要

Machine learning is a branch of computer science, which oversees the study and construction of algorithms that learn from data. Out of the various machine-learning concepts, this paper talks about 6 clustering algorithms: k-means, DBSCAN, OPTICS, Agglomerative, Divisive and COBWEB. The paper incorporates the performance analysis of these clustering algorithms when applied to FAO Soya bean dataset. The algorithms are compared on the basis of various parameters, such as time taken for completion, number of iterations, and number of clusters formed and the complexity of the algorithms. Finally, based on the analysis, the paper determines the best befitting algorithm for the FAO Soya bean dataset.