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

Cluster Evaluation of Density Based Subspace Clustering