clustering


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clus·ter

 (klŭs′tər)
n.
1. A group of the same or similar elements gathered or occurring closely together; a bunch: "She held out her hand, a small tight cluster of fingers" (Anne Tyler).
2. Linguistics Two or more successive consonants in a word, as cl and st in the word cluster.
3. A group of academic courses in a related area.
v. clus·tered, clus·ter·ing, clus·ters
v.intr.
To gather or grow into bunches.
v.tr.
To cause to grow or form into bunches.

[Middle English, from Old English clyster.]

Clustering

 a group gathered together in a cluster.
Examples: clustering of calamities, 1576; of humble dwellings, 1858; of verdure, 1842; clustering together in companies, 1541.
ThesaurusAntonymsRelated WordsSynonymsLegend:
Noun1.Clustering - a grouping of a number of similar thingsclustering - a grouping of a number of similar things; "a bunch of trees"; "a cluster of admirers"
agglomeration - a jumbled collection or mass
knot - a tight cluster of people or things; "a small knot of women listened to his sermon"; "the bird had a knot of feathers forming a crest"
swad - a bunch; "a thick swad of plants"
tuft, tussock - a bunch of hair or feathers or growing grass
Translations
References in periodicals archive ?
3(a) and 3(b) show the stability performance of Lowest-ID based 1-hop clustering, Highest-degree based 1-hop clustering, MOBIC based 1-hop clustering, MobDHop based 2-hop clustering, and DWCM based 2-hop clustering when the number of vehicles varies from 50 to 75 with a fixed S = 20 m/s and [T.sub.r] = 200 m.
These figures show that multi-hop clustering approaches (e.g., MobDHop and DWCM) form obviously fewer clusters than 1-hop clustering approaches (e.g., Lowest-ID, Highest-Degree, and MOBIC) in the simulation.
Clustering is an important discovery technique of exploratory data mining and a common technique for statistical data analysis.
Existing research works have proposed the use of hard clustering and fuzzy based approaches for clustering the vehicles.
proposed LEACH (Low-Energy Adaptive Clustering Hierarchy) in which sensors have a probability of becoming a cluster head without message exchange [1].
Clustering is the process of assigning a homogeneous group of objects into subsets called clusters, so that objects in each cluster are more similar to each other than objects from different clusters based on the values of their attributes [1].
Clustering is an important technique of exploratory data mining, which divides a set of objects into several groups in such a way that objects in same group are more similar with each other in some sense than with the objects in other groups.
Results show that the K-Means cluster obtains a best cluster "quality" with higher S-KL indexes on the whole and continuous seismic clustering areas.
Then, when we evaluate the result of the clustering algorithm, we also want to know whether the result can reflect the user's intent or not [2].
Key words: Data clustering, Partitioned-based clustering algorithms, K-means, Initial centroids.
The proposed algorithm utilizes the fuzzy C-means (FCM) clustering algorithm to automatically identify the damaged area and also combines the TV model to realize image automatic inpainting.

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