**Linkage methods** decide how to measure distance between clusters when building clusters step-by-step. Different linkages create **different cluster shapes**.

Which linkage method is known for creating long, chaining clusters and is most sensitive to noise?

Clustering ist eine der grundlegenden Techniken des maschinellen Lernens, die es Ihnen ermöglicht, viele komplexe Probleme im realen Leben zu lösen: Marktsegmentierung, Anomalieerkennung, Dimensionsreduktion, Aufdecken versteckter Muster usw.

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.

Clusteranalyse

Linkages

Clusteranalyse

Linkages