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Optimization and improvement of data mining algorithm based on efficient incremental kernel fuzzy clustering for large data

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Abstract

The arrival of the big data era in the new century has made the traditional data mining algorithms unable to meet the requirements of big data mining in accuracy and efficiency. Therefore, a data mining algorithm based on efficient incremental kernel fuzzy clustering for big data was optimized—in this paper. First of all, the methods of big data mining and fuzzy clustering technique for data mining were summarized. Then, the data mining algorithm based on the incremental kernel fuzzy clustering was optimized. Finally, the method was validated by comparing with the stKFCM algorithm. The verification results showed that the improved algorithm was superior in performance and accuracy, but only a slight gap in running time.

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Acknowledgements

This work was supported by Science and Technology Program of Colleges and Universities of Shandong province (J15LN11), China; Key projects of Education Department of Shandong Province (C2016M058), China; Key projects of Education Department of Sichuan Province (16ZA0090), China; National Social Science Fund Project (17BGL058), China; Humanity and Social Science Research Foundation of Ministry of Education (15YJA790051), China.

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Authors and Affiliations

  1. School of Information Technology, Shandong Women’s University, Jinan, 250300, China

    Cuifen Zhang

  2. College of Earth Sciences, Chengdu University of Technology (CDUT), Chengdu, 610059, China

    Lina Hao

  3. Key Laboratory of Geoscience Spatial Information Technology, Ministry of Land and Resources of the People’s Republic of China, Chengdu, 610059, China

    Lina Hao

  4. Shandong Jianzhu University, Jinan, 250101, China

    Li Fan

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  1. Cuifen Zhang
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Correspondence to Lina Hao.

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Zhang, C., Hao, L. & Fan, L. Optimization and improvement of data mining algorithm based on efficient incremental kernel fuzzy clustering for large data. Cluster Comput 22 (Suppl 2), 3001–3010 (2019). https://doi.org/10.1007/s10586-018-1767-1

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  • DOI: https://doi.org/10.1007/s10586-018-1767-1

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