| Autor: |
Jing Hu, Xianbin Xu |
| Jazyk: |
angličtina |
| Rok vydání: |
2019 |
| Předmět: |
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| Zdroj: |
EURASIP Journal on Wireless Communications and Networking, Vol 2019, Iss 1, Pp 1-6 (2019) |
| Druh dokumentu: |
article |
| ISSN: |
1687-1499 |
| DOI: |
10.1186/s13638-019-1441-1 |
| Popis: |
Abstract The data distribution in big data environment is very different, and it is difficult to mine the data because of the strong interference of redundant data and frequent items. The traditional data mining algorithm uses closed frequent item feature extraction algorithm. Due to the uneven distribution of web data in big data environment, the mining accuracy of closed frequent item feature extraction is not high. A real-time web data mining model is proposed based on high order spectral feature fuzzy neural network learning in big data environment. The transmission channel model and statistical time series model of web data under big data environment are constructed, and the redundant information flow is removed and reprocessed, and the web data after redundant filtering is analyzed by fusion clustering. The feature of high order spectrum is extracted, and the optimal mining of web data is realized by using fuzzy neural network learning classification method. The simulation results show that this web data mining method has good timeliness, high mining precision, and superior performance. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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