Clustering

Clustering is a technique used in machine learning to group similar data points together based on their characteristics or attributes.

Clustering by Clustering (CbC) is a specific clustering approach that iteratively applies clustering algorithms to refine the clusters.

CbC starts with an initial clustering using a chosen algorithm, then extracts features or representatives from each cluster and applies another clustering algorithm on these representatives. This process is repeated until convergence.

CbC can potentially produce more accurate and meaningful clusters compared to a single clustering algorithm. It helps in overcoming limitations in the initial clustering and improves cluster quality over iterations.

CbC is useful when the clusters obtained from a single clustering algorithm are not satisfactory or when a higher level of refinement is required. It can be applied to various domains like data mining, pattern recognition, and information retrieval.

"Any clustering algorithm can be used as long as it can take representatives or features as input, including k-means, hierarchical clustering, DBSCAN, or spectral clustering.

Representatives are crucial in CbC as they provide condensed information about each cluster. The subsequent clustering algorithms operate on these representatives instead of the original data points.

The number of iterations in CbC can be predetermined based on the problem domain or determined heuristically by monitoring cluster quality changes across iterations.

Some challenges include determining the optimal number of clusters, selecting appropriate clustering algorithms, and avoiding the risk of overfitting. It also requires careful consideration of feature selection and convergence criteria.

You can refer to academic research papers and literature on clustering algorithms or consult online resources and tutorials specific to Clustering by Clustering.