Unsupervised learning is a type of algorithm that learns patterns from untagged data. The intention is that the machine is forced to build a compact internal representation of its world through imitation. This interesting topic came to us from Tech Target in their article, “Unsupervised machine learning: Dealing with unknown data.”
With unsupervised learning, the algorithm and model are subjected to “unknown” data”. When data is unknown, the machine learning system must teach itself to classify the data. The majority of practical machine learning uses supervised learning.
Between supervised and unsupervised learning is semi-supervised learning, where the teacher gives an incomplete training signal: a training set with some (often many) of the target outputs missing.
In unsupervised machine learning, clustering is the most common process used to identify and group similar entities or items together. Grouping similar data points helps to create a more accurate profile and attributes for different groups. Clustering can also be used to reduce the dimensionality of the data when there are significant amounts of data.
Melody K. Smith
Sponsored by Access Innovations, the world leader in thesaurus, ontology, and taxonomy creation and metadata application.