Clustering groups similar data points. To do this, you need to measure the **"distance"** between points. Distance measures tell you how **alike** or **different** data points are. Choosing the right distance measure is important. 

We'll look at two common distance measures: **Euclidean distance** and **Manhattan distance**. 

## Euclidean Distance 

**Euclidean distance** is like measuring the straight-line distance between two points. Imagine you are looking at a map and measuring the **distance between two cities** as the crow flies. That's Euclidean distance. It's the most common way to measure distance. 

Think of it simply as "as the crow flies" distance. It works well when you want to know the direct distance and **all directions are equally important**.  

For example, if you have two points, think of using a ruler to measure straight between them.

## Manhattan Distance 

**Manhattan distance** is like measuring distance in a city where you have to walk along city blocks. You **can't go diagonall**y through buildings; you have to walk along streets. It's also called **city block** distance. That's exactly Manhattan distance. 

Think of it as walking city blocks. It's useful when movement is restricted to **horizontal** and **vertical** directions, or when you want to be less sensitive to big differences in just one direction.

Which distance measure is most appropriate when movement is restricted to horizontal and vertical directions?

Gain a solid understanding of cluster analysis, a key unsupervised learning technique for uncovering patterns in unlabeled data. Explore the essentials of K-Means, Hierarchical Clustering, DBSCAN, and GMMs, and get hands-on experience with real datasets to build confidence in applying clustering to real-world problems.

Dive into the fundamentals of clustering and discover how it differs from classification. Explore essential algorithms, tools, and libraries that power this unsupervised learning technique to uncover hidden patterns in data.

Gain a solid understanding of key preprocessing techniques that ensure effective clustering. Learn how to handle missing values, encode categorical features, normalize data, and choose appropriate distance measures and linkages to boost clustering accuracy.

Master the skills needed to apply K-Means clustering effectively. Learn how the algorithm works, determine the optimal number of clusters, and gain hands-on experience by implementing K-Means on both synthetic and real-world datasets.

Explore the essentials of hierarchical clustering and learn how to group data into meaningful clusters using dendrograms. Build confidence in identifying the optimal number of clusters and implementing the technique on both synthetic and real-world datasets.

Discover how DBSCAN excels at detecting clusters of varying shapes and handling noise in data. Learn the mechanics behind this density-based algorithm, how to assign points to clusters, and apply it to both synthetic and real datasets with confidence.

Gain a solid understanding of Gaussian Mixture Models and how they use probability to model complex cluster shapes. Learn the principles of Gaussian distribution, explore how GMMs work, and build confidence by applying them to both dummy and real-world data.

Cluster Analysis

Distance Measures

Euclidean Distance

Manhattan Distance