A Data Mining Algorithm For Gene Expression Data
... and iteratively move points between clusters until some local minimum is found with respect to some distance metric between each point and the center of the cluster it belongs to. Hierarchical Clustering: These methods start with each point being considered a cluster and recursively combine pairs of ...
... and iteratively move points between clusters until some local minimum is found with respect to some distance metric between each point and the center of the cluster it belongs to. Hierarchical Clustering: These methods start with each point being considered a cluster and recursively combine pairs of ...
Improving Clustering Performance on High Dimensional Data using
... Clustering is an unsupervised process of grouping elements together. A cluster is a collection of data objects that are similar to one another within the same cluster and are dissimilar to the objects in other clusters. There are different clustering techniques available in the literature, such as h ...
... Clustering is an unsupervised process of grouping elements together. A cluster is a collection of data objects that are similar to one another within the same cluster and are dissimilar to the objects in other clusters. There are different clustering techniques available in the literature, such as h ...
Ensemble of Clustering Algorithms for Large Datasets
... One of the most effective approaches to clustering large datasets is the so-called grid-based approach [3], which involves transition from clustering of individual objects to clustering of the elements of the grid structure (cells) formed in an attribute space. This approach assumes that all objects ...
... One of the most effective approaches to clustering large datasets is the so-called grid-based approach [3], which involves transition from clustering of individual objects to clustering of the elements of the grid structure (cells) formed in an attribute space. This approach assumes that all objects ...
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.