Summary  
This chapter explains the differences between supervised classification and unsupervised clustering, focusing on their objectives, data labeling, outputs, and typical algorithm families.  

General domain of usage  
Customer segmentation

**Clustering** and **classification** are different machine learning techniques with distinct goals. 

**Classification** is about sorting into known categories (like sorting mail into pre-labeled boxes). **Clustering**, on the other hand, is about discovering categories (like finding groups in unsorted mail).

Classification is commonly used in **spam detection** or **image recognition**, where the categories are predefined. On the other hand, clustering is used in scenarios like **customer segmentation** or **discovering topics in a collection of documents**, where the goal is to reveal hidden patterns or groupings.

In short, classification is about predicting **known categories**, while clustering helps discover **unknown groupings**. The choice between the two depends on the nature of your data and the problem you're trying to solve.

Which of the following best highlights the difference between classification and clustering?

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.

Clustering Vs Classification

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Clustering Vs Classification