Affiliations: [a] National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu, China
| [b]
Nuclear Power Institute of China, Chengdu, China
| [c]
Information Management Department of West China Second University Hospital, Chengdu, China
| [d] School of Aeronautics and Astronautics and College of Computer Science, Sichuan University, Chengdu, China
Correspondence:
[*]
Corresponding author. Junfeng Wang, School of Aeronautics and Astronautics and College of Computer Science, Sichuan University, Chengdu, China. E-mail: wangjf@scu.edu.cn.
Abstract: With the rapid development of information technology, the era of big data has come. This paper focuses on the analysis and processing of data. How to mine valuable information in large data has been widely concerned by mining frequent itemsets in the data to derive. Association rules are an important part of data mining technology. The analysis of complex network structure based on improved botnet algorithm is the basis of network science. The study of complex network structure is helpful to understand and predict large data of complex network function and behavior. The conclusion of the system in the botnet algorithm process analysis provides some directional suggestions for the improvement of search engine functions and services. In theory, a set of simple and easy botnet algorithm for analyzing the satisfaction factors of clustering is proposed. The practice proves that the big data intelligent search engine based on differential evolution botnet algorithm has significant improvement in retrieval time, recall rate and precision, and can effectively realize personalized and accurate search. A complete big data mining system is implemented, which provides an efficient and easy-to-use tool for data mining botnet algorithms on large data sets.
Keywords: Botnet algorithm, big data intelligent search, engine
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