Description Usage Arguments Value Examples
Function Implementing KSeeds. K-Seeds, firstly randomly chooses a number of drugs (renamed Seeds) equal to the number of clusters desired. Then, the other drugs are assigned to a cluster with respect to Hamming Distance between the drug and the seed of a certain cluster. Cluster seeds are not recomputed at each iteration. This allows a speed up in terms of computational complexity and the algorithm terminates when all the drugs have been assigned.
1 | KSeedsClusters(train, num_clusters, Seed, s)
|
train |
train matrix of features |
num_clusters |
number of clusters desired |
Seed |
subset of drugs features matrix, with just the Seeds as rows |
s |
the seeds of the clusters |
clusters list indicating the cluster to which each drug belongs to
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