Contents
How should we choose the number of clusters in hierarchical clustering?
The optimal number of clusters can be defined as follow:
- Compute clustering algorithm (e.g., k-means clustering) for different values of k.
- For each k, calculate the total within-cluster sum of square (wss).
- Plot the curve of wss according to the number of clusters k.
Do we need to define the number of clusters in advance for hierarchical clustering?
Hierarchical clustering does not require you to pre-specify the number of clusters, the way that k-means does, but you do select a number of clusters from your output.
How to determine the number of clusters in hierarchical clustering?
In order to identify the correct number of clusters to return from a hierarchical clustering/segmentation algorithm, we introduce the L method. The definition of a “cluster” is not well-defined, and measuring cluster quality is subjective.
Is there such thing as objectively best clustering?
There is no such thing as the objectively best clustering. Thus, there also is no objectively best number of clusters.
Is the R-Hierarchical Clustering Method an objective task?
Cluster analysis in itself is not an objective task. A clustering may be mathematically good, but useless. A clustering may score much worse mathematically, but it may provide you insight to your data that cannot be measured mathematically. You can also try the R-NN Curves method. http://rguha.net/writing/pres/rnn.pdf
How is the knee determined in hierarchical clustering?
The knee is determined by finding the area between the two lines that most closely fit the curve. The L method only requires the clustering/segmentation algorithm to be run once, and the overhead of determining the number of clusters is trivial compared to the runtime of the clustering/segmentation algorithm.