Comparative Study of Hierarchical Clustering over Partitioning
... hierarchical algorithm. An agglomerative hierarchical method begins with each object as its own cluster. It then successively merges the most similar clusters together until the entire set of data becomes one group .In order to determine which groups should be merged in agglomerative hierarchical cl ...
... hierarchical algorithm. An agglomerative hierarchical method begins with each object as its own cluster. It then successively merges the most similar clusters together until the entire set of data becomes one group .In order to determine which groups should be merged in agglomerative hierarchical cl ...
Introduction to data mining - Laboratoire d`Infochimie
... N00: number of instances couple in different clusters for both clustering N11: number of instances couple in same clusters for both clusters N01: number of instances couple in different clusters for the first clustering and in the same clusters for the second N10: number of instances couple in the s ...
... N00: number of instances couple in different clusters for both clustering N11: number of instances couple in same clusters for both clusters N01: number of instances couple in different clusters for the first clustering and in the same clusters for the second N10: number of instances couple in the s ...
Nearest-neighbor chain algorithm
In the theory of cluster analysis, the nearest-neighbor chain algorithm is a method that can be used to perform several types of agglomerative hierarchical clustering, using an amount of memory that is linear in the number of points to be clustered and an amount of time linear in the number of distinct distances between pairs of points. The main idea of the algorithm is to find pairs of clusters to merge by following paths in the nearest neighbor graph of the clusters until the paths terminate in pairs of mutual nearest neighbors. The algorithm was developed and implemented in 1982 by J. P. Benzécri and J. Juan, based on earlier methods that constructed hierarchical clusterings using mutual nearest neighbor pairs without taking advantage of nearest neighbor chains.