Practical AI Techniques for Unsupervised Learning—Including Cluster Analysis and KNN Classification—Demonstrated with MATLAB Examples

Description

In the field of Artificial Intelligence, we can highlight two types of learning that are widely used to train machines and devices to understand a set of data: supervised learning and unsupervised learning. On the other hand, unsupervised learning is more closely aligned with Artificial Intelligence as it gives the idea that a machine can learn to identify complex processes and patterns without the need for a human to provide guidance and supervision throughout the learning process. Some examples of unsupervised learning algorithms include clustering and, association rules, and kNN cassifiers. In the case of this type of learning, there is no pre-training data set; the problem is approached blindly and only with logical operations to guide it. Although at first glance it seems impossible, it is about the ability to solve complex problems using only input data and logical algorithms. This avoids the use of reference data.

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