IN DATA STREAMS USING CLASSIFICATION AND CLUSTERING DIFFERENT TECHNIQUES TO FIND NOVEL CLASS

Abstract

Data stream mining is a process of extracting knowl edge from continuous data. Data Stream classificati on is major challenges than classifying static data because of several unique p roperties of data streams. Data stream is ordered s equence of instances that arrive at a rate does not store permanently in memory. The problem making more challenging when concept drift occurs when data changes over time Major problems of data stream mining is : infinite length, concept drift, concept evolution. Novel class detection in data stream classification is a interesting research top ic for concept drift problem here we compare differ ent techniques for same