A data-stream-based abnormal data mining in web texts environment
| Autor: | Ye-Zheng Liu, Jin-Yun Wang, Jin-Kun Wang |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Data stream
Process (engineering) Computer science General Engineering Stability (learning theory) 020206 networking & telecommunications 02 engineering and technology computer.software_genre Computer Science Applications Computational Mathematics Resource (project management) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining Resource consumption computer |
| Zdroj: | Journal of Computational Methods in Sciences and Engineering. 16:361-368 |
| ISSN: | 1875-8983 1472-7978 |
| Popis: | The stability and self-adaption for combination texts must be processed in Web Texts Environment. Therefore a language and technology method for self-adapting environment of web texts is needed. To do this, we have built an adaptive data-stream method in which the abnormal data mining process is started. The resource consumption of abnormal data in a text includes the resource consumption of error text and the total resource consumptions of relating with the previously executed texts which are dependent on the error text. In this paper an adaptive data-stream method is applied to implement the Abnormal Data Mining in Web Texts Environment. Proved by simulation verification, we proposed this adaptive data-stream method is efficient for solving the problem of abnormal data mining in web texts environment. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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