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Title:
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PARTITIONING OF INTERVAL-TYPE DATA SETS USING KERNEL FUZZY C-MEANS CLUSTERING ALGORITHM |
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Author(s):
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Anderson F. B. F. da Costa, Cesar Rocha Vasconcelos, Bruno A. Pimentel, Renata M. C. R. de Souza, Reinaldo Gomes |
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ISBN:
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978-989-8533-20-3 |
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Editors:
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Hans Weghorn |
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Year:
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2013 |
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Edition:
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Single |
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Keywords:
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Kernel, clustering, interval-type data, fuzzy, data mining. |
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Type:
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Full Paper |
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First Page:
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92 |
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Last Page:
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98 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Kernel clustering methods have been very important in application of non-supervised machine learning and data mining to real problems. Kernel methods possess many advantages other than nonlinearity such as modularity, ability to work with heterogeneous descriptions of data, incorporation of prior knowledge etc. In this paper, we introduced a kernel-based fuzzy clustering method for partitioning a set of interval-type data. In addition, this method is compared to a fuzzy partitioning approach for interval-type data introduced previously. Experiments with real interval-type data sets are presented. The evaluation of the clustering results furnished by the methods is performed regarding the computation of an external cluster validity index and the global error rate of classification. |
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