View PDF - International Journal of Computer Science and Mobile
... proceeds as follows. First, it randomly selects k of the objects, each of which initially represents a center. For each of the remaining objects, an object is assigned to the cluster to which it is the most similar, based on the distance between the object and the cluster. It then computes the new m ...
... proceeds as follows. First, it randomly selects k of the objects, each of which initially represents a center. For each of the remaining objects, an object is assigned to the cluster to which it is the most similar, based on the distance between the object and the cluster. It then computes the new m ...
Extraction of Best Attribute Subset using Kruskal`s Algorithm
... general graph-theoretic clustering is basic: Compute an area graph of instances, at that point delete of any edge in the diagram that is much longer/shorter than its neighbours. The result is a backwoods and every tree forest represents a cluster. In our study, we apply graph theoretic clustering me ...
... general graph-theoretic clustering is basic: Compute an area graph of instances, at that point delete of any edge in the diagram that is much longer/shorter than its neighbours. The result is a backwoods and every tree forest represents a cluster. In our study, we apply graph theoretic clustering me ...
A Novel Optimum Depth Decision Tree Method for Accurate
... center. The CRT-1 representatives are randomly selected from frequently occurring classes and CRT-2 representatives are also selected randomly from those which frequently occurred in every class, showing poorer performance than the other types. Hence it is proved that random selection may not give t ...
... center. The CRT-1 representatives are randomly selected from frequently occurring classes and CRT-2 representatives are also selected randomly from those which frequently occurred in every class, showing poorer performance than the other types. Hence it is proved that random selection may not give t ...
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.