A Clustering Method Based on Attribute Reduction and SOM
In Insurance industry, data redundancy is an extremely common problem in the population statistics. As a result a satisfactory clustering quality can rarely be obtained with the traditional clustering method. To handle this kind of problems a clustering model based on attributes reduction and SOM neural network was proposed. Using attributes reduction rules redundant information can be easily distinguished and essential attributes effectively located. And therefore the clustering quality can also be improved evidently. Experiments conducted in the H life insurance company show the method can cope with the problems mentioned above effectively.
Y. Q. Zheng et al., "A Clustering Method Based on Attribute Reduction and SOM", Applied Mechanics and Materials, Vols. 58-60, pp. 1001-1006, 2011