Identifying exceptions from data streams based on kernel estimation and interval clustering
Dingrong Yuan · Jisuanji kexue yu tansuo · 2007
It proposes a strategy for mining abnormal burst patterns from data streams using a sliding window with bounded resources(such as memory constraints).Design a compact data structure,TTI,which consists of three nested tiers of time intervals,for monitoring data entrance into the sliding window so as to identify the current abnormity at any time.The threshold for identifying abnormal items in the approach is dynamically generated by an algorithm KIC(kernel estimation and confidence interval clustering),whereas existing algo rithms use predefined(static)thresholds.This leads to mote accurate outputs.Based on the threshold,an algo- rithm SWMA is designed to reduce time and space complexities.The approach is evaluated by conducting ex- periments on a simulated linear model,a non-linear model and a real time series data stream.It demonstrates that the method is efficient and promising.