Xiang LI, Yuan LI, Zifei ZHANG, et al. A density clustering-based network performance failure big data analysis algorithm[J]. Telecommunications science, 2020, 36(9): 51-58.
DOI:
Xiang LI, Yuan LI, Zifei ZHANG, et al. A density clustering-based network performance failure big data analysis algorithm[J]. Telecommunications science, 2020, 36(9): 51-58. DOI: 10.11959/j.issn.1000-0801.2020270.
A density clustering-based network performance failure big data analysis algorithm
how to quickly find abnormal data in massive monitoring database and carry out network failure analysis becomes a research difficulty.A density-based network performance failure big data analysis algorithm was proposed
which extracted key performance characteristic indicators through entropy weight analysis
implemented data shaping through data cleaning and standardization
and extracted abnormal performance data on the basis of DBSCAN clustering algorithm.Relying on the real-time massive backbone network link performance data of multiple domestic operators to validated this algorithm
the results shows that compared with the manually manner
the recognition accuracy of the algorithm proposed to the network performance abnormal data is more than 90%
which can well fit for the analysis of real-time Internet network operation failure.
CAI Z R . Application of entropy weight based fuzzy comprehensive evaluation method in learning quality evaluation [J ] . Computer Era , 2018 .( 12 ): 75 - 77 .
LIU J Y , ZHANG K , WANG G H . Comparative study on data standardization methods in comprehensive evaluation [J ] . Digital Technology & Application , 2018 , 36 ( 6 ): 84 - 85 .
ZHOU A W , YU Y F . The research about clustering algorithm of K-means [J ] . Computer Technology and Development , 2011 , 21 ( 2 ): 62 - 65 .
闫玮 . 基于多种层次聚类的算法研究 [D ] . 西安:西安电子科技大学 , 2019 .
YAN W . Algorithms research based on multiple hierarchical clustering [D ] . Xi’an:Xidian University , 2019 .
ESTER M , KRIEGEL H P , XU X . A density-based algorithm for discovering clusters adensity-based algorithm for discovering clusters in large spatial databases with noise [C ] // Proceedings of International Conference on Knowledge Discovery & Data Mining . New York:ACM Press , 1996 .
SUN P , HAN C D , ZENG T . S-DBSCAN:an algorithm for finding high density clusters based on DBSCAN [J ] . Chinese High Technology Letters , 2012 , 22 ( 6 ): 589 - 595 .
SHAH G H , . An improved DBSCAN,a density based clustering algorithm with parameter selection for high dimensional data sets [C ] // Proceedings of Nirma University International Conference on Engineering . Piscataway:IEEE Press , 2013 .
FENG Z H . Research and application of clustering algorithm based on DBSCAN [D ] . Wuxi:Jiangnan University , 2016 .
SUNITA J , PATAG K . Algorithm to determine ε-distance parameter in density based clustering [J ] . Expert Systems With Applications , 2014 ,( 6 ): 2939 - 2946 .
FENG W X , ZHU Y , GUO J T , et al . Lightning forecast based on the improved DBSCAN and polynomial fitting [J ] . Computer Engineering and Science , 2014 , 36 ( 10 ): 2028 - 2033 .