WANG Shu-xia, XIONG Zeng-gang. Huge Amounts of Data Under the Interference of Dangerous Web Data Mining Technology Research[J]. 2016, 33(2): 88-92.DOI:
Huge Amounts of Data Under the Interference of Dangerous Web Data Mining Technology Research
Put forward a lot of data under the dangerous web data mining algorithm based on self-organizing mapping
through massive amounts of data error between the predicted values and the actual value of the judgment and exclude the interference of data
on this basis
through self-organization feature mapping networks for dangerous web data mining
self-organizing feature map network was introduced and the detailed process of the output layer competition
determine a network
can form mapping will be dangerous web data mining as a self-organizing map network input vector input
the output wins produced the corresponding points on the graph
similar input vector convergence in adjacent areas of the map
distance and the regions victory points corresponding to the input vector is dangerous web data can be judge.The simulation results show that the proposed algorithm for huge amounts of data under the interference of dangerous web data mining
not only has high efficiency
and also has high performance in mining precision.