Abstract
Clustering is traditionally viewed as an unsupervised method for data analysis. However, in some cases information about the problem domain is available in addition to the data instances themselves. To make use of this information, in this paper, we develop a new clustering method “MLP-KMEANS” by combining Multi-Layer Perceptron and K-means. We test our method on several data sets with partial constrains available. Experimental results show that our method can effectively improve clustering accuracy by utilizing available information.
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Guan, D., Yuan, W., Lee, YK., Gavrilov, A., Lee, S. (2007). Combining Multi-layer Perceptron and K-Means for Data Clustering with Background Knowledge. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques. ICIC 2007. Communications in Computer and Information Science, vol 2. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74282-1_137
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DOI: https://doi.org/10.1007/978-3-540-74282-1_137
Publisher Name: Springer, Berlin, Heidelberg
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