Detecting Change in Data Streams Daniel Kifer, Shai Ben-David, and Johannes Gehrke   View Paper (PDF)   Return to RESEARCH SESSION 5:STREAM MINING Abstract Detecting changes in a data stream is an important area of research with many applications. In this paper, we present a novel method for the detection and estimation of change. In addition to providing statistical guarantees on the reliability of detected changes, our method also provides meaningful descriptions and quantification of these changes. Our approach assumes that the points in the stream are independently generated, but otherwise makes no assumptions on the nature of the generating distribution. Thus our techniques work for both continuous and discrete data. In an experimental study we demonstrate the power of our techniques. ©2005 Association for Computing Machinery