Contents
- 1 What is an appropriate distance measure to use for hierarchical clustering?
- 2 What is hierarchical clustering explain any two techniques for finding distance between the clusters in hierarchical clustering?
- 3 Which is the best metric for hierarchical clustering?
- 4 When do you merge two clusters for linkage?
What is an appropriate distance measure to use for hierarchical clustering?
Euclidean distance
For most common hierarchical clustering software, the default distance measure is the Euclidean distance. This is the square root of the sum of the square differences. However, for gene expression, correlation distance is often used. The distance between two vectors is 0 when they are perfectly correlated.
What is hierarchical clustering explain any two techniques for finding distance between the clusters in hierarchical clustering?
In single linkage hierarchical clustering, the distance between two clusters is defined as the shortest distance between two points in each cluster. For example, the distance between clusters “r” and “s” to the left is equal to the length of the arrow between their two closest points.
How is average linkage used in hierarchical clustering?
Along with average-linkage, it is one of the more popular distance metrics. Average-linkage is where the distance between each pair of observations in each cluster are added up and divided by the number of pairs to get an average inter-cluster distance.
Which is the best metric for hierarchical clustering?
Average-Linkage. Average-linkage is where the distance between each pair of observations in each cluster are added up and divided by the number of pairs to get an average inter-cluster distance. Average-linkage and complete-linkage are the two most popular distance metrics in hierarchical clustering.
When do you merge two clusters for linkage?
For the Single linkage, two clusters with the closest minimum distance are merged. This process repeats until there is only a single cluster left. For the Complete linkage, two clusters with the closest maximum distance are merged. This process repeats until there is only a single cluster left.
How to calculate the distance of a cluster?
Choose a distance function for clusters – for clusters formed by just one point, D should reduce to d. Start from N clusters, each containing one item. Then, at each iteration: a) using the current matrix of cluster distances, find two closest clusters.