What are clustering algorithms?
Clustering algorithms are a set of methods used in unsupervised learning to group similar data points together based on certain features or characteristics.
Example: Imagine we have a dataset of customer purchase history with features like age, income, and purchase frequency. Using a clustering algorithm like K-Means, we can group customers into segments such as high-income frequent buyers, young occasional buyers, and so on. This helps businesses target their marketing strategies more effectively.
Decision Trees vs Clustering Algorithms vs Linear Regression
In machine learning, Decision Trees, Clustering Algorithms, and Linear Regression stand as pillars of data analysis and prediction. Decision Trees create structured pathways for decisions, Clustering Algorithms group similar data points, and Linear Regression models relationships between variables. In this article, we will discuss how each method has distinct strengths, making them indispensable tools in understanding and extracting insights from complex datasets.