CSC475 Music Information Retrieval
... Evaluation of clustering is more challenging than classification and requires much more subjective analysis. Many criteria have been proposed for this purpose. They can be grouped into internal (no external information is required) and external (external partition information about the “correct” clu ...
... Evaluation of clustering is more challenging than classification and requires much more subjective analysis. Many criteria have been proposed for this purpose. They can be grouped into internal (no external information is required) and external (external partition information about the “correct” clu ...
A study of the grid and density based algorithm clustering
... between them. Then we can adopt the popular depth-first search arithmetic, or the width-first arithmetic to complete this mission. Therefore, then we realize that the key mission to establish is a data construction that can express the diagram. What I adopt is a matrix that can express the diagram, ...
... between them. Then we can adopt the popular depth-first search arithmetic, or the width-first arithmetic to complete this mission. Therefore, then we realize that the key mission to establish is a data construction that can express the diagram. What I adopt is a matrix that can express the diagram, ...
Ch8-clustering
... A good clustering based on samples will not necessarily represent a good clustering of the whole data set if the sample is biased ...
... A good clustering based on samples will not necessarily represent a good clustering of the whole data set if the sample is biased ...
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.