Optimal partitioning is an iterative re-allocation algorithm to maximize the ratio of within-cluster similarity/among-cluster similarity for a given number of clusters. Optpart can operate as either a crisp (classical) partitioning, or a fuzzy partitioning algorithm.
optpart(x, dist, maxitr = 100, mininc = 0.001, maxdmu = 1)an integer, integer vector, factor vector, or objects of class ‘clustering’, ‘partana’, ‘partition’ or ‘stride’
the maximum number of iterations to perform
the minimum increment in the within/among similarity ratio to continue iterating
the ‘maximum delta mu’. If 1, a crisp (non-fuzzy) partition results. If (0,1) a fuzzy partition results.
an object of class partana, a list with elements:
a matrix of item mean similarity to each cluster
a matrix of mean cluster-to-cluster similarity
a matrix of membership of each item to each cluster. If maxdmu
is 1, this will be a single 1 in the appropriate cluster and 0 in all others. If
maxdmu is (0,1) then the musubx represent fuzzy memberships in each cluster.
a vector giving the cluster each item is assigned to. If optpart is run as a fuzzy partitioning, this is determined by the maximum membership observed.
the vector of within/among similarities achieved at each iteration. The final non-zero value is the final ratio achieved.
the number of iterations performed
the names of the items clustered
optpart produces a partition, or clustering, of items into clusters by iterative reallocation of items to clusters so as to maximize the within cluster/ among cluster similarity ratio. At each iteration optpart ranks all possible re-allocations of a sample from one cluster to another. The re-allocation that maximizes the change in the within-cluster/among-cluster ratio is performed. The next best reallocation is considered, and if it does not include any clusters already modified, it is also performed, as re-allocations of independent clusters are independent and additive in effect. When no further re-allocations can be performed in that iteration, the algorithm recalculates all possible re-allocations and iterates again. When no re-allocations exist that improve the within/among ratio greater than ‘mininc’, or the maximum number of iterations is reached, the algorithm stops.
optpart is designed to run from a random start or the levels of a factor, or
preferably from existing initial partitions. Specifying a single integer gives the number of clusters
desired using a random start. Specifying an integer vector gives the initial assignments of
items to clusters. Initial assignments can also be extracted from a number of objects.
Specific
methods exist for objects of class ‘clustering’ from functions
slice or archi, class ‘partana’ from function
partana, class ‘stride’ from stride, or
class ‘partition’ from functions
pam or diana. optpart is deterministic from a
given initial condition. To get good results from a random start, multiple
random starts should be attempted, using function bestopt.
Optpart is an unweighted algorithm, i.e. each of the \((n^2-n)/2\) pairwise distances or dissimilarities is included in the calculation of the ratio exactly once. Optpart somewhat penalizes small clusters, as small clusters contribute only \((n_i^2-n_i)/2\) values to the numerator; the extreme case is that a cluster with a single member does not contribute anything to the numerator.
It is an interesting characteristic of optpart that no minimum cluster size is enforced, and it is common for partitions of a large number of clusters to contain null clusters, i.e. clusters with no members. This is not a bug or error, but rather an indication that a partition with a fewer number of clusters achieves a better within/among similarity ratio than does a larger number. It is also somewhat common that for solutions with a small or intermediate number of clusters, optpart places outliers in a small ‘trash’ cluster.
When optpart is run as a fuzzy partitioning algorithm, it often achieves a surprisingly low entropy, with many items assigned completely to a single cluster.
partana
# NOT RUN {
data(shoshveg)
dis.bc <- dsvdis(shoshveg,'bray/curtis')
opt.5 <- optpart(5,dis.bc)
summary(opt.5)
# }
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