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Learn about single-linkage clustering, a method of hierarchical clustering based on grouping the closest pairs of elements. See the algorithm, the dendrogram, and the working example with a genetic distance matrix.
Learn about complete-linkage clustering, a method of agglomerative hierarchical clustering that uses the farthest distance between clusters to merge them. See the algorithm, a working example, and the comparison with other linkage schemes.
As with complete linkage and average distance, the difficulty of calculating cluster distances causes the nearest-neighbor chain algorithm to take time and space O(n 2) to compute the single-linkage clustering. However, the single-linkage clustering can be found more efficiently by an alternative algorithm that computes the minimum spanning ...
Agglomerative clustering is a bottom-up approach that merges clusters as one moves up the hierarchy. It uses a distance metric and a linkage criterion to decide which clusters to merge. See examples, algorithms, and complexity of agglomerative clustering.
Ward's method is a statistical technique for hierarchical cluster analysis that minimizes the total within-cluster variance. It can be implemented by a Lance–Williams algorithm and has variations such as Ward p that use cluster specific feature weights.
Cluster analysis is the task of grouping a set of objects based on their similarity or distance. Learn about different cluster models, such as connectivity, centroid, distribution, density and subspace, and their corresponding algorithms, such as hierarchical, k-means, expectation-maximization and DBSCAN.
A comprehensive overview of the tools and resources for RNA-Seq data analysis, from design to quality control, alignment, annotation, differential expression and more. Find links to web pages, software packages and papers for each step of the process.
For EST data, clustering is important to group sequences originating from the same gene before the ESTs are assembled to reconstruct the original mRNA. Some clustering algorithms use single-linkage clustering, constructing a transitive closure of sequences with a similarity over a particular threshold.