Unsupervised learning

Unsupervised learning

Unsupervised learning is a kind of algorithm in machine learning used in the drawing of inference from the dataset that comprises of data input but without labeled output. In simple words, unsupervised learning is an algorithm that does not require to be guided in its operations; it can learn by itself. Some of the most common unsupervised learning algorithm is cluster analysis (Barlow, 1989). Cluster analysis is used in the aggregation of data, and it is very helpful to the supply chain and delivery/ distribution department in a company.

In bioinformatics, unsupervised learning is used for genetic clustering and analysis of sequences. One of the primary goals of unsupervised learning is the clustering of data based on the different group’s characteristics (Dy & Brodley, 2004). When compared to the supervised machine learning, unsupervised learning is more challenging and takes effort to master, for instance, the rate at which one can learn to solve regression model – linear one and the rate at which one can learn to solve k-means clustering through Euclidian distance is different due to variation in complexity.

Another application I want to talk about is the ecology application that read about recently. Audio recordings are clustered through microphones in selected places of the region of interest through a series of clustering techniques. The records are then taken and analyzed using different unsupervised learning techniques, to find out the different number of species in the area of interest.

References

Barlow, H. B. (1989). Unsupervised learning. Neural computation, 1(3), 295-311.

Dy, J. G., & Brodley, C. E. (2004). Feature selection for unsupervised learning. Journal of machine learning research, 5(Aug), 845-889.6