[Data] Application of Unsupervised Learning

Introducing tips for selecting the appropriate algorithm that fits the data!

Unsupervised learning is useful when you want to investigate data in detail but do not have a specific goal in mind regarding what you can learn from the results, or when the information contained in the data is unclear. This document provides a detailed explanation of the hard clustering and soft clustering algorithms used in unsupervised learning. Additionally, we will introduce tips for selecting the appropriate algorithm for your data and methods for reducing the number of features in your dataset to improve model performance. [Contents] ■ When to consider unsupervised learning ■ Unsupervised learning methods ■ Common hard clustering algorithms ■ Common soft clustering algorithms ■ Improving models through dimensionality reduction ■ Common dimensionality reduction techniques *For more details, please refer to the PDF document or feel free to contact us.

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