Proof for using Consensus Clustering
Existing clustering techniques have inherent limitations, making the interpretation of results challenging, particularly when the number of clusters is unknown. Clustering methods are highly sensitive to the initial settings, leading to the amplification of non-significant data in non-reiterative methods.
It is very difficult to validate clustering results in the absence of an external objective criterion (such as known class labels in supervised analysis). Without these standards, this validation becomes elusive. Certain drawbacks of hierarchical clustering are addressed by techniques like SOM and k-means, which offer clearly defined boundaries and clusters.
Consensus clustering provides a way to visualize cluster details, calculate cluster numbers, evaluate stability, and represent consensus across multiple clustering runs. Nevertheless, the number of clusters needs to be selected in advance, and it does not have the same intuitive appeal as hierarchical dendrograms.
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.
Clustering
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