Detection of Outliers and Hubs Using Minimum Spanning Tree
... are detected using a given distance measure on feature space, a point q in a data set is an outlier with respect to the parameters M and d, if there are less than M points within the distance d from q, where the values of M and d are determined by the user. The problem in distance–based approach is ...
... are detected using a given distance measure on feature space, a point q in a data set is an outlier with respect to the parameters M and d, if there are less than M points within the distance d from q, where the values of M and d are determined by the user. The problem in distance–based approach is ...
Swarm Intelligence Algorithms for Data Clustering
... globe are coming up with new algorithms, on a regular basis, to meet the increasing complexity of vast real-world datasets. A comprehensive review of the state-of-the-art clustering methods can be found in (Xu and Wunsch, 2005) and (Rokach and Maimon, 2005). Data mining is a powerful new technology, ...
... globe are coming up with new algorithms, on a regular basis, to meet the increasing complexity of vast real-world datasets. A comprehensive review of the state-of-the-art clustering methods can be found in (Xu and Wunsch, 2005) and (Rokach and Maimon, 2005). Data mining is a powerful new technology, ...
New Outlier Detection Method Based on Fuzzy Clustering
... authors use a hierarchical clustering technique. A similar approach was reported in [34]. Acuna and Rodriguez [33] performed the PAM algorithm [16] followed by the Separation Technique (henceforth, the method will be termed PAMST). The separation of a cluster A is defined as the smallest dissimilari ...
... authors use a hierarchical clustering technique. A similar approach was reported in [34]. Acuna and Rodriguez [33] performed the PAM algorithm [16] followed by the Separation Technique (henceforth, the method will be termed PAMST). The separation of a cluster A is defined as the smallest dissimilari ...
Novel Approach for Heart Disease verdict Using Data Mining
... Abstract: Nowadays Heart Disease is one of the main causes of death in and around countries. Several studies with different technologies have been made in diagnosis and treatment ofheart disease, which includes association rules, logistic regression, fuzzy modeling, Decision tree and neural network. ...
... Abstract: Nowadays Heart Disease is one of the main causes of death in and around countries. Several studies with different technologies have been made in diagnosis and treatment ofheart disease, which includes association rules, logistic regression, fuzzy modeling, Decision tree and neural network. ...
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.