Mining Algorithm for Abnormal Traffic Time Points for Unstructured High-Dimensional Big Data
Generally,unstructured data has a high dimension.Each sample contains a large number of features,leading to dimensionality reduction,so it is difficult to maintain effective feature extraction.Therefore,a new method for mining abnormal time points in high-dimensional unstructured big data traffic based on spatial mapping was put forward.First of all,a sparse regression model was built by using the geometric characteristics of the approximate so-lution set.And then,the sparse projection matrix mapping from high-dimensional space to low-dimensional subspace was solved.Moreover,based on the density distribution,a high-density set was selected as the candidate set of the clustering center,thus determining the initial clustering center for clustering.Meanwhile,a pruning algorithm was ap-plied to all the clusters.Furthermore,a candidate set of time points was selected.After that,a secondary judgment was performed on the candidate set.Finally,the abnormal time points in high-dimensional big data traffic were mined suc-cessfully.Experimental results prove that dimensionality reduction of data can effectively improve the mining accuracy of abnormal traffic.In comparison,the proposed method is more accurate and time-efficient in mining abnormal time points of high-dimensional big data traffic.
Non-structuralHigh-dimensional big dataRate of flowAbnormal time pointMining method
NETL
NSTL
万方数据
