Summary Statistics
We can calculate two summary statistics to assess the stability of a cluster and the importance of specific observations within it. The first statistic, cluster consensus that calculates the average consensus value for every pair of observations within the clusters.
Cluster Consensus = [Tex]\frac{Number\; of \; items\; clustered\; together\; in \;multiple\; runs }{total \;number\; of \;runs }[/Tex]
The next statistic is item consensus which centers on a specific item or observation. It calculates the average consensus value of that item with respect to all other items within its cluster.
Item Consensus = [Tex]\frac{Number \;of\;times\;a\; data \;point \;is\; consistently \; assigned\; to \;the\; same\; cluster}{Total\; number\; of\; runs}[/Tex]
The stability and dependability of clusters as well as individual data points in consensus clustering can be assessed quantitatively using these formulas. They are usually applied in the analysis of the consensus matrix that is produced after several iterations of clustering.
Consensus Clustering
In this article, we’ll begin by providing a concise overview of clustering and its prevalent challenges. Subsequently, we’ll explore how consensus clustering serves as a solution to mitigate these challenges and delve into interpreting its results. Before learning Consensus Clustering, we must know what Clustering is.
In Machine Learning, Clustering is a technique used for grouping different objects in separated clusters according to their similarity, i.e. similar objects will be in the same clusters, separated from other clusters of similar objects. It is an Unsupervised learning method. Few frequently used Clustering algorithms are K-means, K-prototype, DBSCAN etc.
Table of Content
- Issues with the existing clustering Methods
- Proof for using Consensus Clustering
- Consensus Clustering
- Working of Consensus Clustering
- Summary Statistics
- Advantages of Consensus Clustering
- Disadvantages of Consensus Clustering
- Frequently Asked Questions on Consensus Clustering