A METHOD FOR DISTRIBUTED CONCEPT DRIFT DETECTION

Abstract

The paper introduces a method for distributed concept drift detection for data mining algorithms. Concept drift is understood as any unpredictable alteration in input data. There is an algorithm implementation proposed, based on MapReduce distributed computing technology. Proposed algorithm meant for concept drift detection in streaming data in online fashion. In order to provide iterative Map and Reduce phases a MapReduce framework is introduced. The algorithm is able to automatically detect input data alteration, which demands model parameters change and switching a new model online.