ProClus: The ProClus Algorithm for Projected Clustering
Description
The ProClus algorithm works in a manner similar to K-Medoids.
Initially, a set of medoids of a size that is proportional to k is chosen.
Then medoids that are likely to be outliers or are part of a cluster that is
better represented by another medoid are removed until k medoids are left.
Clusters are then assumed to be around these medoids.
Usage
ProClus(data, k = 4, d = 3)
Arguments
data
A Matrix of input data.
k
Number of Clusters to be found.
d
Average number of dimensions in which the clusters reside
References
C. C. Aggarwal and C. Procopiuc Fast Algorithms for
Projected Clustering. In Proc. ACM SIGMOD 1999.