What is k-means clustering for?

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Supervised and Unsupervised Learning are two primary types of machine learning, differing mainly in how they process and learn from data.

Neural networks are a type of machine learning model inspired by the structure and function of the human brain. They are designed to recognize patterns and relationships in data through a process of learning.

K-means clustering is a popular unsupervised machine learning algorithm used for grouping similar data points into clusters. It helps uncover hidden patterns or structures in unlabeled datasets.

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What it's for:

1. Data Segmentation / Grouping

   • Automatically groups data into K distinct clusters based on feature similarity.

   • Example: Segmenting customers based on purchase behavior.

2. Pattern Recognition

   • Identifies natural groupings in data without prior labeling.

   • Example: Grouping genes with similar expression patterns in biology.

3. Image Compression

   • Reduces the number of colors in an image by clustering similar colors.

   • Example: Representing millions of pixels with only K color centroids.

4. Anomaly Detection

   • Points far from any cluster centroid might be considered anomalies or outliers.

   • Example: Fraud detection.

5. Recommendation Systems

   • Group users/items to suggest content based on cluster membership.

   • Example: Grouping users with similar viewing habits on a streaming platform.

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