Classifying Streaming Data II: Time-Dependent Data

Abstract

This chapter builds on the description in Chapter 21of the H-Tree algorithm for classifying streaming data, i.e. data which arrives (generally in large quantities) from some automatic process over a period of days, months, years or potentially forever. Chapter 21was concerned with stationary data generated from a fixed causal model; Chapter 22 is concerned with data that is time-dependent, where the underlying model can change from time to time, perhaps seasonally. This phenomenon is known as concept drift.