Abstract
The main concept of rough sets theory is clustering similarities of objects based on the notions of indiscernibility relation. In this paper, we develop the concept of indiscernibility level of rough set theory as an additional measurement for hierarchical clustering. The combination between indiscernibility (quantitative indiscernibility relation) and indiscernibility level are used as a new method for hierarchical clustering. The indiscernibility level quantifies the indiscernibility of pairs of objects among other objects in information system. For comparison, the following four clustering methods were selected and evaluated on a simulation data set : average-, complete- and single-linkage agglomerative hierarchical clustering and Ward’s method. The simulation shows that the hierarchical clustering yields dendrogram instability that gives different solutions under permutations of input order of data objects. The result of this paper shows that the new method plays an important role in clustering information system and compared to other method, clustering based on indiscernibility and its indiscernibility level reduces the dendrogram instability.
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Hakim, R.B.F., Subanar, Winarko, E. (2010). The Concept of Indiscernibility Level of Rough Set to Reduce the Dendrogram Instability. In: Zhang, Y., Cuzzocrea, A., Ma, J., Chung, Ki., Arslan, T., Song, X. (eds) Database Theory and Application, Bio-Science and Bio-Technology. BSBT DTA 2010 2010. Communications in Computer and Information Science, vol 118. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17622-7_21
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DOI: https://doi.org/10.1007/978-3-642-17622-7_21
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