Abstract
This paper proposes a framework to get a stable classification rule under unsupervised learning, and the term “stable” means that the rule remains unchanged when the sample set increases. This framework initially makes use of clustering analysis and then use the result of clustering analysis as a reference-studying sample. Secondly, AdaBoost integrated several classification methods is used to classify the samples and get a stable classification rule. To prove the method feasible, this paper shows an empirical study of classifying retail outlets of a tobacco market in a city of China. In this practice, k-means is used to make clustering analysis, and AdaBoost integrated RBF neural network, CART, and SVM is used in classification. In the empirical study, this method successfully divides retail outlets into different classes based on the sales ability.
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Notes
- 1.
Please contact me for the data if anybody wants to continue research on this problem.
- 2.
Here accuracy rate is defined as 1- error rate. As the sum of d ij is less than than 100 %, the accuracy is slightly larger than the actual accuracy
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Acknowledgments
We are very grateful to our project mentor Prof. Defu Zhang for his great support on algorithms in the data mining field. This work has been partially supported by the National University Student Innovation Program of China and the National Natural Science Foundation of China (Grant No. 61272003).
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Liu, K., Wang, B., Lin, X., Ma, Y., Xing, J. (2014). A Retail Outlet Classification Model Based on AdaBoost. In: Patnaik, S., Li, X. (eds) Proceedings of International Conference on Soft Computing Techniques and Engineering Application. Advances in Intelligent Systems and Computing, vol 250. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1695-7_42
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DOI: https://doi.org/10.1007/978-81-322-1695-7_42
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