Abstract
Traditional data mining application is an iterative feedback process which suffers from over-depending on both business and data specialists’ decision ability. This paper studies the variable-scale decision making problem based on the scale transformation theory. We propose the numerical concept space to model significant information and knowledge after business and data understanding. An algorithm of variable-scale clustering is also put forth. A case study on TYL product management demonstrates that our method is able to achieve accessible and available performance in practice.
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Wang, A., Gao, X., Yang, M. (2020). Variable-Scale Clustering Based on the Numerical Concept Space. In: Zhang, J., Dresner, M., Zhang, R., Hua, G., Shang, X. (eds) LISS2019. Springer, Singapore. https://doi.org/10.1007/978-981-15-5682-1_20
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DOI: https://doi.org/10.1007/978-981-15-5682-1_20
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Online ISBN: 978-981-15-5682-1
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