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The K-means algorithm is a method for dividing a set of data points into distinct clusters, or groups, based on similar attributes. It is an unsupervised learning algorithm which means it does not require labeled data in order to find patterns in the dataset.
K-means is an approachable introduction to clustering for developers and data scientists interested in machine learning. In this article, you will learning how to implement k-means entirely from scratch and gain a strong understanding of the k-means algorithm.
- What is Clustering?
- How to Define Similarity in a Cluster?
- Characteristics of a Good Similarity Function
- Overview of Common Clustering Methods
- How does K-means Clustering work visually?
- What is the K-means Pseudocode?
- How to write K-means from Scratch in Python?
- Image Segmentation with K-means algorithm
- Choosing the Proper Number of Clusters
- Test Your Understanding