Advanced Clustering
Learning Outcomes
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What’s Included
Course Introduction
Course Introduction
In this lesson, we get a preview of the course ahead.
Preparing Data for Clustering
Preparing Data for Clustering
Often your data won't be immediately ready to perform clustering. Learn how to apply the good practice principle of feature scaling your data and visualize it to look for any naturally apparent clusters.
Understanding Single Linkage Clustering
Understanding Single Linkage Clustering
K-means is often a very useful clustering algorithm, but in certain circumstances, it cannot accurately cluster your data. When this is the case, it's useful to know about Single Linkage Clustering as this uses a different approach to segmenting data.
Performing Single Linkage Clustering
Performing Single Linkage Clustering
Learn how to perform Single Linkage Clustering on your data in Python.
Understanding Soft Clustering
Understanding Soft Clustering
Often hard clustering algorithms like k-means don't give us an accurate representation, especially when there are many data points that are close to multiple clusters. In these cases, we can turn to soft clustering to better represent borderline data points.
Performing Soft Clustering
Performing Soft Clustering
Learn how to perform Soft Clustering on your own data using the Fuzzy C Means algorithm.
Evaluating a Soft Clustering Model
Evaluating a Soft Clustering Model
Use a custom function based on the scatter function to plot your soft clusters using varying transparency levels to represent probability.
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