Description Usage Arguments Details Value References Examples
Perform clustering on circular data to minimize the withincluster sum of squared distances.
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O 
a vector of circular data points. They can be coordinates along the circle based on distance, or angles around the circle. 
K 
the number of clusters 
Circumference 
the circumference of the circle where data are located 
method 
the circular clustering method.

By circular data, we broadly refer to data points on any nonselfintersecting loop.
In clustering N circular points into K clusters, the "FOCC" algorithm
is reproducible with runtime O(K N log^2 N)
\insertCiteDebnath21OptCirClust;
The "HEUC" algorithm, not always reproducible, calls the kmeans
function repeatedly;
The "BOCC" algorithm with runtime O(KN^2), reproducible but slow, is done via
repeatedly calling the Ckmeans.1d.dp
function.
An object of class "CirClust"
which has a plot
method. It is a list with the following components:
cluster 
a vector of clusters assigned to each element in 
centers 
a numeric vector of the means for each cluster in the circular data. 
withinss 
a numeric vector of the withincluster sum of squares for each cluster. 
size 
a vector of the number of elements in each cluster. 
totss 
the total sum of squared distances between each element and the sample mean. This statistic is not dependent on the clustering result. 
tot.withinss 
the total sum of withincluster squared distances between each element and its cluster mean. This statistic is minimized given the number of clusters. 
betweenss 
the sum of squared distances between each cluster mean and sample mean. This statistic is maximized given the number of clusters. 
ID 
the starting index of the frame with minimum SSQ 
Border 
the borders of 
Border.mid 
the middle point of the last and first points of two consequitive clusters. 
O_name 
a character string. The actual name of the 
Circumference 
the circumfarence of the circular or periodic data. 
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