clustering algorithm

clustering algorithm

[¦kləs·tə·riŋ ¦al·gə‚rith·əm]
(computer science)
A computer program that attempts to detect and locate the presence of groups of vectors, in a high-dimensional multivariate space, that share some property of similarity.
References in periodicals archive ?
Moreover, a detecting and clustering algorithm based on Principal Direction Divisive Partitioning (PDDP) and K-means with in this framework is shown and evaluated.
In proposed system the Network On-chip is divided into local clusters by using partition clustering algorithm, and then threshold value can be set at each and every buffer in the cluster.
The use of a K-means clustering algorithm to look at distinct events, volume of suspicious and compromised events and the weight for categories results in alerts from SIEM on an hourly basis.
So we prefer to apply the clustering algorithm to extracting the dosing data of normal with weighing units as the normal data, owing to the fact that the normal weighing units are in the majority of all the units, and then the fault data can be extract based on the normal data.
We have shown how the increased amount and variety of data from natural user interfaces can be exploited to acquire contextual information by developing a biometric user identification method and a clustering algorithm for hand detection, both for multitouch displays.
Among the algorithms that have been there for fuzzy clustering, we will use the popular fuzzy clustering algorithm C - Means [20, 21].
Her and Dr Gaber applied a clustering algorithm to 21 years of environmental data from the UK and two countries of comparable population size - France and Italy - to enable trends to become more visible to authorities.
Following Feser, Koo and Nolan et al, we used the Ward agglomerative hierarchical clustering algorithm to identify and categorize occupations into clusters.
The empirical strategy consists of applying a clustering algorithm to Census 2000 data describing U.
The DifFUZZY unsupervised clustering algorithm is applied at the initial stage, giving an accuracy of 96.
The topics include prominent machine learning and data mining methods with example applications to the medical domain, cancer prediction methodology using an enhanced artificial neural network-based classifier and dominant gene expression, a penalized fuzzy clustering algorithm with its application in magnetic resonance image segmentation, seven discretization techniques used for rule induction from data on the lazy eye vision disorder, and applying artificial intelligence in minimally invasive surgery and artificial palpitation.
The number of classes for the K-means clustering algorithm is automatically computed from the initial image with a covariance-based approach.