关键词: 数据流, 核估计, 置信区间聚类分析, 异常模式, 滑动窗口

Abstract:

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 more accurate outputs.Based on the threshold，an algorithm SWMA is designed to reduce time and space complexities.The approach is evaluated by conducting experiments 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．

Key words: data streams, kernel estimation, confidence interval clustering, exception patterns, sliding windows