Time-focused density-based clustering of trajectories of moving
... A reachability threshold is needed in order to locate clusters (and noise) The threshold for the largest I is manually set by the user ...
... A reachability threshold is needed in order to locate clusters (and noise) The threshold for the largest I is manually set by the user ...
Discovery of Climate Indices using Clustering
... – They extract climate variability at a regional or global scale into a single time series. – They are well-accepted by Earth scientists. – They are related to well-known climate phenomena such as El Nino. ...
... – They extract climate variability at a regional or global scale into a single time series. – They are well-accepted by Earth scientists. – They are related to well-known climate phenomena such as El Nino. ...
Efficient Analysis of Pharmaceutical Compound Structure Based on
... A. Single-link clustering This method that consider the distance between two clusters to be equal to the shortest distance from any member of one cluster to any member of the other cluster. If the data consist of similarities, the similarity between a pair of clusters is considered to be equal to th ...
... A. Single-link clustering This method that consider the distance between two clusters to be equal to the shortest distance from any member of one cluster to any member of the other cluster. If the data consist of similarities, the similarity between a pair of clusters is considered to be equal to th ...
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.