In unsupervised learning, the algorithm goes through the data itself and tries to come up with meaningful results. The result might be, for example, a set of clusters of data points that could be related within each cluster. That works better when the clusters don’t overlap. Machine learning algorithms explained | Martin Heller - InfoWorld
Unlike supervised learning, unsupervised learning is used with data sets without historical data. An unsupervised learning algorithm explores surpassed data to find the structure. This kind of learning works best for transactional data; for instance, it helps in identifying customer segments and clusters with certain attributes—this is often used in content personalization. Popular techniques where unsupervised learning is used also include self-organizing maps, nearest neighbor mappig, singular value decomposition, and K-Means clustering. Basically, online recommendations, identification of data outliers, and segment text topics are all examples of unsupervised learning. Machine Learning: What it is and Why it Matters | Priyadharshini @ simplilearn
There are many types of unsupervised learning, although there are two main problems that are often encountered by a practitioner: they are clustering that involves finding groups in the data and density estimation that involves summarizing the distribution of data.
- Clustering: Unsupervised learning problem that involves finding groups in data.
- Density Estimation: Unsupervised learning problem that involves summarizing the distribution of data.
Clustering and density estimation may be performed to learn about the patterns in the data.
Additional unsupervised methods may also be used, such as visualization that involves graphing or plotting data in different ways and projection methods that involves reducing the dimensionality of the data.
- Visualization: Unsupervised learning problem that involves creating plots of data.
- Projection: Unsupervised learning problem that involves creating lower-dimensional representations of data.
An example of a visualization technique would be a scatter plot matrix that creates one scatter plot of each pair of variables in the dataset. An example of a projection method would be Principal Component Analysis (PCA) that involves summarizing a dataset in terms of eigenvalues and eigenvectors, with linear dependencies removed. 14 Different Types of Learning in Machine Learning | Jason Brownlee - Machine Learning Mastery