{"id":201497,"date":"2024-01-24T10:30:10","date_gmt":"2024-01-24T15:30:10","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=201497"},"modified":"2024-01-24T10:30:53","modified_gmt":"2024-01-24T15:30:53","slug":"hierarchical-clustering-in-python-a-comprehensive-implementation-guide-part-ii","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/hierarchical-clustering-in-python-a-comprehensive-implementation-guide-part-ii\/","title":{"rendered":"Hierarchical Clustering in Python: A Comprehensive Implementation Guide – Part II"},"content":{"rendered":"\n

See Part I<\/a> for an overview.<\/em><\/p>\n\n\n\n

Key concepts of hierarchical clustering<\/h2>\n\n\n\n

Let us find the key concepts of hierarchical clustering before moving forward since these will help you with the in-depth learning of hierarchical clustering.<\/p>\n\n\n\n

How to identify if two clusters are similar?<\/h3>\n\n\n\n

One of the ways to do so is to find the distance between clusters.<\/p>\n\n\n\n

Measure of distance (similarity)<\/strong><\/p>\n\n\n\n

The distance between two clusters can be computed based on the length of the straight line drawn from one cluster to another. This is commonly known as the Euclidean distance.<\/p>\n\n\n\n

The Euclidean distance between two points in either the plane or 3-dimensional space measures the length of a segment connecting the two points. It is the most obvious way of representing distance between two points.<\/p>\n\n\n\n

If the (x1,y1) and (x2,y2) are points in 2-dimensional space then the euclidean distance between is:<\/p>\n\n\n\n

(x2-x1)2 – (y2-y1)2<\/p>\n\n\n\n

Other than Euclidean distance, several other metrics have been developed to measure distance such as:<\/p>\n\n\n\n