Cluster Evaluation of Density Based Subspace Clustering

Sembiring, Rahmat Widia and Jasni, Mohamad Zain (2010) Cluster Evaluation of Density Based Subspace Clustering. Journal of Computing, Volume 2, (Issue 11, November 2010). pp. 14-19. ISSN 2151-9617. (Published)

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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.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty/Division: Faculty of Computer System And Software Engineering
Depositing User: Mr. Rahmat Widia Sembiring
Date Deposited: 25 Apr 2011 03:10
Last Modified: 22 May 2018 02:32
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