This notebook provides a guide to Clustering Analysis, an unsupervised learning tasks, where algorithms have to automatically learn patterns from data by themselves as no target variables are defined beforehand.
When a dataset does not provide a target variable the use of Clustering Analysis Algorithms would uncover natural patterns by grouping similar data points.
Clustering Analysis Algorithms are well known and applied in the data science industry for grouping and highlighting similar data points, detecting outliers and showing known and unknown patterns in the dataset.
Some of the common uses of Clustering Analysis Algorithms are:
- Fraud detection by identifying unusual clusters from the data.
- Uncover natural patterns by grouping similar data points.
- Prediction of new tendencies on natural individuals.