In Unsupervised Learning, the aim is to draw patterns and regularities from the input data and the dataset and to use them to gain insights and conclusions for further processing. To determine regularities and trends, methods are used to aggregate the input data into clusters. In other words, the system determines fixed patterns, which are then used as a reference for pattern recognition, thus enabling objects or data to be assigned to a group. The method, which is effective in extracting the structure of data and allows computers to automatically find the correlations and patterns from huge amounts of data, is also used in data mining.
The goal of unsupervised learning is to determine relationships, structures, and relations between data. The Unsupervised Learning model builds on existing data structures and integrates new data into the existing model. A disadvantage of unsupervised learning is that it is controlled because what is to be learned depends on the computers. The analysis depends on the quality of the data provided and the algorithm used for clustering.