Advantages of Consensus Clustering
The advantages of Consensus clustering include:
- Robustness: Consensus Clustering enhances the robustness of clustering results by aggregating information from multiple runs, reducing sensitivity to initialization.
- Stability Assessment: It helps to identify clusters that are consistently present across various iterations by providing a quantitative measure of cluster stability.
- Cluster Validation: Consensus Clustering aids in the validation of clusters by offering insights into the reliability and significance of identified clusters.
- Noise Reduction: It assists in filtering out noise or less stable clusters by capturing consensus patterns, producing clustering results that are more dependable.
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