张凯斐, 刘继华, 张菊芳. 大规模高维数据集中局部异常数据挖掘算法[J]. 微电子学与计算机, 2018, 35(3): 116-119, 124.
引用本文: 张凯斐, 刘继华, 张菊芳. 大规模高维数据集中局部异常数据挖掘算法[J]. 微电子学与计算机, 2018, 35(3): 116-119, 124.
ZHANG Kai-fei, LIU Ji-hua, ZHANG Ju-fang. Local Outlier Mining Algorithm for Large Scale High Dimensional Data Set[J]. Microelectronics & Computer, 2018, 35(3): 116-119, 124.
Citation: ZHANG Kai-fei, LIU Ji-hua, ZHANG Ju-fang. Local Outlier Mining Algorithm for Large Scale High Dimensional Data Set[J]. Microelectronics & Computer, 2018, 35(3): 116-119, 124.

大规模高维数据集中局部异常数据挖掘算法

Local Outlier Mining Algorithm for Large Scale High Dimensional Data Set

  • 摘要: 提出一种基于FFD的大规模高维数据集中局部异常数据挖掘算法.将FFD首次应用在挖掘中, 通过引用无线传输技术, 将所提方法的宗旨定为对作业级与任务级的实现, 以提高局部异常数据抗干扰能力.所提方法利用FFD的强控制能力实现无线传输技术与挖掘进程的数据互通, 利用FIFO挖掘思想依次进行数据本地化与挖掘, 并对挖掘流程与目标函数进行了重点设计.实验结果证明, 所提方法的可靠性强, 挖掘效率高, 挖掘任务完成量大.

     

    Abstract: This paper proposes a mining algorithm of large scale and high dimensional data based on FFD local concentration of abnormal data. The FFD was first applied in mining, by referencing the wireless transmission technology, the proposed method is the aim for the realization of the working class and the task level, in order to improve the anti-jamming ability of local abnormal data. The data exchange method based on robust control ability of FFD wireless transmission technology and the data mining process, followed by local mining and using FIFO mining method and the mining process and the objective function of the key The experimental results show that the proposed method has strong reliability, high mining efficiency and large amount of mining tasks.

     

/

返回文章
返回