Abstract
The original form of the Rough c-means algorithm does not distinguish between data points in the boundary area. This paper presents an extended Rough c-means algorithm in which the distinction between data points in the boundary area is captured and used in the clustering procedure. Experimental results indicate that the proposed algorithm can yield more desirable clustering results in comparison to the original form of the Rough c-means algorithm.
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Li, F., Liu, Q. (2011). An Extension to Rough c-Means Clustering. In: Yao, J., Ramanna, S., Wang, G., Suraj, Z. (eds) Rough Sets and Knowledge Technology. RSKT 2011. Lecture Notes in Computer Science(), vol 6954. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24425-4_29
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DOI: https://doi.org/10.1007/978-3-642-24425-4_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24424-7
Online ISBN: 978-3-642-24425-4
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