  Clustering real world data often faced with curse of dimensionality, where
real world data often consist of many dimensions. Multidimensional data
clustering evaluation can be done through a density-based approach. Density
approaches based on the paradigm introduced by DBSCAN clustering. In this
approach, density of each object neighbours with MinPoints will be calculated.
Cluster change will occur in accordance with changes in density of each object
neighbours. The neighbours of each object typically determined using a distance
function, for example the Euclidean distance. In this paper SUBCLU, FIRES and
INSCY methods will be applied to clustering 6x1595 dimension synthetic
datasets. IO Entropy, F1 Measure, coverage, accurate and time consumption used
as evaluation performance parameters. Evaluation results showed SUBCLU method
requires considerable time to process subspace clustering; however, its value
coverage is better. Meanwhile INSCY method is better for accuracy comparing
with two other methods, although consequence time calculation was longer.
