How are clusters grouped based on the distance between them?
During clustering, starting with single-member clusters, the clusters are merged based on the distance between them. There are many different ways to define distance between clusters, and based on which definition you use, the hierarchical clustering results change.
How is similarity determined in hierarchical agglomerative clustering?
Hierarchical Agglomerative Clustering (HAC) Assumes a similarity function for determining the similarity of two clusters. Starts with all instances in a separate cluster and then repeatedly joins the two clusters that are most similar until there is only one cluster. The history of merging forms a binary tree or hierarchy.
How to Cluster patients based on their similarity?
We need to define a distance or similarity metric between patients’ expression profiles and use that metric to find groups of patients that are more similar to each other than the rest of the patients. This, in essence, is the general idea behind clustering.
How are the sub-clusters merged in clustering?
The method argument defines the criteria that directs how the sub-clusters are merged. During clustering, starting with single-member clusters, the clusters are merged based on the distance between them.
How does feature scaling affect the clustering results?
Scaling affects Clustering Results in a way that depends by the metric used (Euclidean Distance, Squared Euclidean Distance, Manhattan Distance, …) In general when you are mixing features which have different physical measurements units, you can think of a Linear Transformation (i.e. Offset + Scale Factor) to transform them into a common space
Do you need to scale a clustering algorithm?
Yes. Clustering algorithms such as K-means do need feature scaling before they are fed to the algo. Since, clustering techniques use Euclidean Distance to form the cohorts, it will be wise e.g to scale the variables having heights in meters and weights in KGs before calculating the distance.
Which is an example of a cluster analysis?
Cluster is a group of similar things positioned or occurring closely together, Cluster analysis itself is not one specific algorithm. some examples are: It can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them.