Ensemble learning is a recent and extended
approach to the unsupervised data mining technique called
clustering which is used from finding natunl gmupings that
exist in a dataset. Hetre, we applied an ensemble based
clustering algol'ithm called Random Fot·ests with Pat·tition
amund Medoids (PAM) to multiple time sel'ies gene
expt·ession data of Plasmodium falcipat·um. The Random
Fot·est algol'ithm is most common ensemble leat·ning
appmach that uses decision tt·ees. Random Fm·est consists
of lat·ge numbet· of classification tt·ees (nnging fmm
hundt·eds to thousands) built from rabootstnp
sampling of the dataset. We also applied the following
intemal clustet· validity measures; Silhouette Width index,
Connectivity Index and the Dunn Index to select the optimal
numbet· of final clustet·s. Om· t·esults show that ensemble
based clustering is indeed a good altet·native fm· clustet·
analysis with the premise of an improved performance ovet·
traditional clustering algorithm
