Abstract
Soft clustering plays an important role in many real world applications. Fuzzy clustering, rough clustering, evidential clustering and many other approaches are used effectively to overcome the rigidness of crisp clustering. Each approach has its own unique features that set it apart from others. In this paper, we propose an enhanced rough clustering approach by combining the strengths of rough clustering and evidential clustering. The rough K-means algorithm is augmented with an ability to determine outliers in datasets using the concepts from the Evidential c-means algorithm. Different experiments are carried on various datasets and it is found that the modified rough K-means can effectively detect outliers with relatively smaller computational complexity.
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Joshi, M., Lingras, P. (2013). Enhancing Rough Clustering with Outlier Detection Based on Evidential Clustering. In: Ciucci, D., Inuiguchi, M., Yao, Y., Ślęzak, D., Wang, G. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2013. Lecture Notes in Computer Science(), vol 8170. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41218-9_14
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DOI: https://doi.org/10.1007/978-3-642-41218-9_14
Publisher Name: Springer, Berlin, Heidelberg
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