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Optimization Research of Decision Support System Based on Data Mining Algorithm

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Abstract

In order to analyze and make use of data and information more accurately and efficiently, the clustering algorithm is further studied to a certain extent, especially in the process of clustering, further analyzes and refines the processing data. In the field of application of previous clustering of mathematical statistics such as pattern recognition, do some reference, especially in the ant colony clustering algorithm in data aggregation is proposed based on the principle of solving business decision support system to deal with data in the huge data processing result is not ideal problem. The data processing steps of data mining are studied. Through information entropy and ant colony clustering algorithm to achieve this process, the original data for reasoning and verification is also used and the effect before and after the improvement are compared. At the same time, this research provides effective decision support for website construction in contemporary e-commerce field. Taking e-commerce website browsing path as an example, applying the idea of ant colony clustering algorithm based on information entropy to carry out path analysis, five kinds of path types are got. This study can be used as a reference for the construction of other e-commerce websites.

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Acknowledgements

The authors acknowledge the National Natural Science Foundation of China (Grant: 111578109), the National Natural Science Foundation of China (Grant: 11111121005).

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Authors and Affiliations

  1. Wuchang University of Technology Information and Engineering School, Wuhan, 430223, Hubei, China

    Yuhua Peng, Xiaolan Yang & Wenli Xu

Authors
  1. Yuhua Peng
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  2. Xiaolan Yang
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  3. Wenli Xu
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Corresponding author

Correspondence to Yuhua Peng.

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Peng, Y., Yang, X. & Xu, W. Optimization Research of Decision Support System Based on Data Mining Algorithm. Wireless Pers Commun 102, 2913–2925 (2018). https://doi.org/10.1007/s11277-018-5315-3

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  • DOI: https://doi.org/10.1007/s11277-018-5315-3

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