cluster

(redirected from clustering)
Also found in: Dictionary, Thesaurus, Medical, Legal, Encyclopedia, Wikipedia.

cluster around (someone or something)

1. To gather around someone or something. The kids clustered around the clown to watch him make balloon animals.
2. To position someone or something around someone or something else. In this usage, a noun or pronoun is used between "cluster" and "around." Mom clustered the kids around the clown so that they could all see him make balloon animals. Now we need to cluster the purple flowers around the white ones.
See also: around, cluster

cluster together

To gather or bunch together. The kids clustered together to watch the clown make balloon animals. Why are all of our nicest Christmas ornaments clustered together on one side of the tree?
See also: cluster, together

clusterfuck

1. rude slang A chaotic situation rife with problems. Oh, the dinner party was a total clusterfuck—the roast was dry, the toilet overflowed, and everyone argued about politics.
2. vulgar slang A group sexual encounter. No, I've never taken part in a clusterfuck—have you?

cluster around someone or something

[for a group of people or things] to bunch together, surrounding someone or something. The birds clustered around the chimney top to keep warm. The kids clustered around the police officer.
See also: around, cluster

cluster someone or something around someone or something

to bunch people or things together around someone or something. She clustered the cups around the punch bowl. Karen clustered the children around the fire.
See also: around, cluster

cluster together

to bunch or group together. All of the bats clustered together on the roof of the cave. The children clustered together in small groups here and there on the playground.
See also: cluster, together

cluster fuck

1. n. an act of group rape. (Also Charlie Foxtrot from the initials CF. Usually objectionable.) Look at her! She’s just asking for a cluster fuck.
2. n. any event as riotous as an act of group rape. (Figurative on sense 1 The same allusion as sense 1) This goddamn day has been one long cluster fuck!
See also: cluster, fuck
References in periodicals archive ?
Ansari, "A position-based clustering technique for Ad Hoc Intervehicle communication," IEEE Transactions on Systems, Man and Cybernetics - Part C: Applications and Reviews, vol.
Valaee, "Mobility-based clustering in VANETs using Affinity Propagation," in Proc.
Initial Data Clustering Using Multidimensional Diffusion Density Distribution.
Different attributes of multidimensional data have different units of measurement and value ranges, which has a serious impact on clustering formation.
On the i of sample points [x.sub.i] in the data sets [[mu].sub.i], calculate Euclidean distance between it and the clustering center, and get its category label
Using the above K-means algorithm, cluster analysis was performed on the data obtained, thus draw customer load can be divided into 4 categories obviously, and the clustering center is shown in Figure 5.
However, this scheme incurs more overhead and memory consumption when compared to hard clustering approaches.
The concept of rough set theory developed by a polish scientist Pawlak [19] is a method for making the decision in case of uncertainty or vagueness in clustering. A data set is represented as an information table I = (U, A) where U represents each row of objects and 'A' represents each column of attributes.
Buttyan, "Secure and Reliable Clustering in Wireless Sensor Networks: A Critical Survey," Computer Networks, vol.
(0,1) interval to denote the belongingness of those clustering points, and the uncertainty among patterns lying in the shadowed region is efficiently handled in terms of membership.
When compared with traditional single clustering algorithms, cluster ensemble approaches are able to integrate multiple clustering solutions obtained from different data sources into a unified solution and provide a more robust, stable and accurate final result.
The K-Means cluster can be affected by the initial clustering centers, and the typical K-Means cluster results (K = 11) are shown in Figure 7(a).
Clustering in data mining falls into the category of unsupervised learning, which is to find the interrelationship between the data.
The error percentage criterion was used to compare clustering results of both algorithms.
In the FCM experiment in this paper given gray values of the pixel which in the repaired areas, we can calculate the Euclidean distance of it with each clustering center concluded by formula (8), the cluster which has the minimum distance is the repaired area D.