cluster


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Related to cluster: Cluster analysis, Cluster Headaches

cluster

 [klus´ter]
a group of similar objects, events, or other elements in close proximity.
suicide cluster a group of suicides in which one seems to set off others.
Miller-Keane Encyclopedia and Dictionary of Medicine, Nursing, and Allied Health, Seventh Edition. © 2003 by Saunders, an imprint of Elsevier, Inc. All rights reserved.

clus·ter

(klŭs'ter),
A group of similar or identical objects occurring naturally in close proximity (as grapes) or so assembled (as beads).
[O.E. clyster]
Farlex Partner Medical Dictionary © Farlex 2012

cluster

A generic term for any of the regions in the UK (Eastern, North East, North West and West Midlands, London, and Southern) created after consultation with Strategic Health Authorities (SHAs) on how to best deliver local Information Technology solutions as part of the NHS Care Records initiative.
Segen's Medical Dictionary. © 2012 Farlex, Inc. All rights reserved.

clus·ter

(klŭstĕr)
A group of similar or identical objects occurring naturally in close proximity (as grapes) or so assembled (as beads).
[O.E. clyster]
Medical Dictionary for the Dental Professions © Farlex 2012
References in periodicals archive ?
In Figure 6, nodes 1, 4, and 5 broadcast a cluster separator message to determine a cluster border at the beginning of the second step.
A malicious node may avoid rebroadcasting the message to induce a cluster membership disagreement.
The Incremental Semi-Supervised Cluster Ensemble (ISSCE) approach is adapted to perform the data clustering process.
The clustering process is carried out with the cluster count specified by the user.
Calculate the mean of each cluster by [m.sub.k] = [mathematical expression not reproducible], where [x.sub.i] [member of] [C.sub.k] and [parallel][C.sub.k][parallel] is the data number in cluster [C.sub.k], k = 1, 2, ..., K.
(2) No: output cluster each datum allocates to and list the cluster centroids.
where [d.sub.i] x scl is the cluster center of the data point [d.sub.i] before the movement.
(2011) proposed a method to compute initial cluster centers for k-means with a focus on improving the performance of k-means algorithm by making better initial centroids selection.
In [20], a load-balancing clustering scheme that focuses on cluster maintenance was proposed, with the goal of limiting the number of mobile nodes in each cluster so the clusters have similar sizes.
In iterative clustering algorithms, choosing initial cluster centers is extremely important as it has a direct impact on the formation of final clusters.
Since the clusters are formed based on the traffic flow, stable clusters are maintained and frequent formation of clusters and cluster head can be avoided.