centroid

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centroid

(sĕn′troyd) [″+ ″]
A mathematically calculated center of a complex or three-dimensional object (e.g., the left ventricle of the heart, a polypeptide, or a rural county).
References in periodicals archive ?
If the hardware components that are used for running the k-means algorithm are not considered, it can be concluded that the total processing time that the k-means algorithm needs to process a dataset depends on two major things: the position of the initial centroids and the number of the iterations that are performed until an acceptable solution is found.
Furthermore, this centroid is used to generate a cake.
The clustering results obtained with the k-means algorithm using initial centroids calculated by the proposed method were improved.
Cluster 2, on the other hand, has values below the grand centroid, indicating a lack of both technical and entrepreneurial competencies among the students.
At last, the data set is divided by the above mentioned natural cluster centroid, and the principle of dividing the data is still in the form of the formula (18).
Name Classification Aldrin 3 Alpha-hexa 1 Clorbenzilato 2 Clordano 3 DDT 3 Endosulfan 3 Endrin 3 Heptacloro 3 Isobenzan 1 Lindano 1 Metoxicloro 2 Pentaclorofenato 1 TDE 1 Azametifos 1 Azinfos-etil 1 Azinfos-metil 1 Bensulida 1 Bromofos 1 Bromofos-etil 3 Butonato 1 Cadusafos 1 Cianofos 1 Clorfenvinfos 1 Clorpirifos 1 Clorpirifos-metil 2 Diazinon 1 Diclofention 3 Dimethylvinphos 1 Dioxation 1 Edifenfos 1 EPN 3 Etion 1 Etoprop 1 Etrimfos 1 Fenamifos 1 Fenitrotion 1 Fenkaptona 1 Fonofos 1 Fosalon 1 Fosmet 1 Alanycarb 2 Aldicarb 1 Aminocarb 1 Barban 1 Bendiocarb 1 Benfuracarb 1 Carbofurano 1 Carbosulfan 1 Chinometionato 2 Clororofam 2 Desmedifam 2 Fenmedifam 2 Fenobucarb 1 Fenotiocarb 2 Ferbam 2 Furatiocarb 1 Isoprocarb 1 Methiocarb 1 Mexacarbato 1 Pebulato 2 Table 6: Final centroids.
Using the k-means algorithm to cluster sets of documents with initial parameters from the result of minimal closed subsets, the pseudoclosure distance to compute the distance between two objects and the inter-pseudoclosure distance to re-compute the new centroids.
The predetermined LSP 3-subtype centroid predictor was then applied to all 4 data sets, and results were compared with tumor morphologic classifications.
As shown above, the nearest country to every centroid will be considered as representative of its cluster.
Step 3) Repeat Step 2) until the centroids of two groups remain constant.
Clusters Centroids Entities C1 17, 2453 393 C2 28, 5236 105 C3 9, 167 3445 C4 28, 21474 9 C5 15, 1002 1048 Table 2: Multiparty clustering detailed results.