| Abstrak/Abstract |
The simple and fast k-medoids (Fast-KM) is one partition algorithm that consists of three steps: selecting the
initial medoid, updating the medoid, and assigning objects to the final medoids. Though the Fast-KM can adopt any
proximity and update the medoid by minimizing the total distance within groups, it ignores empty clusters that may
appear. This study aims to modify the Fast-KM algorithm so there are no unallocated groups. The modification was
carried out by adding one process in the first steps, namely partitioning similar Fast-KM indicator blocks in ascending
order and taking one representative object from each of the first k-blocks as initial medoids. This study used three real
datasets from the University of California, Irvine (UCI) Machine Learning Repository: primary tumor data, breast cancer
data, and zoo data. The Fast-KM algorithm failed to partition the three datasets according to the number of actual classes,
as indicated by the occurrence of unfilled groups. Conversely, for these datasets, besides similar objects (not or as
medoids) being in the same group in either the initial group or the final group, the proposed method (Modified Fast-KM)
also guarantees no empty clusters. |
