Abstract
Granular computing is a multidisciplinary theory rapidly developed in recent years. It provides a conceptual framework for many research fields, among others data mining. Data mining techniques and algorithms focus on knowledge discovery from data. When data labels are unknown one can use methods of exploratory data analysis called clustering algorithms. Clustering algorithms are also useful to find hidden dependencies and patterns in data. In this article granular computing and clustering were implemented in information granulation system SOSIG and applied to exploration of real medical data set. Data granulation in the system can be performed on different levels of resolution. Thereby the granules composed of clusters reflect relationship between objects on distinct levels of details. The clustering in SOSIG is generated automatically - there is no requirement to give a number of groups for division. It eliminates problems present in popular clustering algorithms like selection of correct number of clusters and evaluation of created partitioning. The difficulties are encountered in most partitioning as well as hierarchical methods reducing their practical application.
Additionally, this article contains solution generated by SOSIG in comparison with clustering results of algorithms: k-means, hierarchical, EM and DBSCAN. There are used quality indices such as Dunn’s, DB, CDbw and SI.
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Kużelewska, U., Stepaniuk, J. (2008). Information Granulation: A Medical Case Study. In: Peters, J.F., Skowron, A., Rybiński, H. (eds) Transactions on Rough Sets IX. Lecture Notes in Computer Science, vol 5390. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89876-4_6
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DOI: https://doi.org/10.1007/978-3-540-89876-4_6
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