Abstract
Although Data Field Clustering method has a lot of advantages, clustering result depends severely on affection factor that is selected in Data Field function. The purpose of the paper is to find an optimum affection factor that may not only reflect nature characteristic of clustering data sample, but also reduce influence caused by sample deviation to minimum. In this paper, an affection interval concept is defined at first. Then an optimum objective function for reducing influence of sample deviation is constructed and an approximate solution is given of optimum affection factor. In the end, a standard data set offered in the MATLAB is used to test the availability of the optimum affection factor, the result is satisfactory.
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Yang, H., Liu, J., Li, Z. (2007). Affection Factor Optimization in Data Field Clustering. In: Zhou, ZH., Li, H., Yang, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2007. Lecture Notes in Computer Science(), vol 4426. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71701-0_115
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DOI: https://doi.org/10.1007/978-3-540-71701-0_115
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71700-3
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