To fully explore abnormal home broadband data

perspicuously understanding abnormal home broadband situation

considering the specific user feedback data of indicators related to abnormal home broadband events

a kind of comprehensive evaluation system including dominant factors and the severity was proposed

and on this basis

with a hierarchical clustering using cosine correlation and Manhattan distance of normalized data respectively

the abnormal data was divided into several different categories.Practice with data from existing network shows that the proposed method can effectively divide a large amount of irregular abnormal home broadband data into different characteristics of several classes

extract the notable abnormal information

and can establish a KNN classification model with high prediction accuracy.