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Partitioning Around Medoids.
A classification based on the Partitioning Around Medoids (PAM) method is used to identify the seven main impact patterns.
The results by the PAM (Partitioning Around Medoids) approach, in particular, reveal good accuracy, with small error for the largest cases.
We used Latent Class Analysis (MCLUST), Partitioning Around Medoids (PAMK) and Hierarchical Agglomeration via Ward's method (HCLUST), presenting the solutions generated by requesting 2, 3 and 4 clusters.
Call and discrimination rates of the additional QC Modified Partitioning Around Medoids algorithm (MCR and MDR) did not differ over ∼20%, indicating no contamination or sample mix-up.
Detection rates were calculated with GTYPE 4.1 (Affymetrix) using the MPAM (Modified Partitioning Around Medoids) MDR algorithm, that represents the number of SNPs that passes the MPAM discrimination filter/total number of SNPs.
Here we employ the classification algorithm Partitioning Around Meloid (PAM) [15] to see if the periods of constant extension can be classified into pauses and subpauses, i.e to see if a cluster of size two is the ideal classification.
Two clustering algorithms were selected: PAM (Partitioning Around Medoids) and K-means (Hartigan and Wong variant).
Quality controls were performed using Affymetric GType software and the MPAM algorithm (Modified Partitioning Around Medoids).
JACOP [ 22] uses the partitioning algorithm implemented under the name PAM (Partitioning Around Medoids) in the R statistical package [ 23].
Similar(1)
A widely used k-medoids heuristic algorithm is the partitioning-around-medoids (PAM) algorithm (Theodoridis and Koutroumbas 2008), which works by randomly selecting k medoids from the original set of n data points and then minimizing the objective function via hill-climbing.
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