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.

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.

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

Mentioned in

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.

Securing cluster formation and cluster head elections in wireless sensor networks

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.

High dimensional data partitioning with an adaptive ensemble construction and analysis scheme

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.

K-Means Cluster for Seismicity Partitioning and Geological Structure Interpretation, with Application to the Yongshaba Mine (China)

(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.

AN AVERAGE-BASED APPROACH FOR INITIAL CENTROID SELECTION IN K-MEANS ALGORITHM

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.

Distributed and weighted clustering based on d-hop dominating set for vehicular networks

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.

Rough set based fuzzy scheme for clustering and cluster head selection in VANET