Empirical Analysis on Stream Classification & Clustering with Concept Drift in MOA - Patel, Hari A; Patel, Harsh N; Bhatt, Nirav (International Journal of Computer Sciences and Engineering, 2025)
Stream data processing is the next ‘big thing’ in big data which is one of the most propagating fields in computer science. The stream data analytics is an important aspect while dealing with data stream mining. While dealing with the classification of stream data, concept drift and its effect are required to be considered. Massive online analysis (MOA) is one of the most popular tools to perform analytics on stream data. We primarily deal with three features which are provided by moa namely classification, clustering & concept drift. The key emphasis is on experimental analysis on the combination of different procedures and learner algorithm which are suited for training the model so it can be used for the prediction purpose. Besides that, we have also tried to identify drift (change) in data and its effect on performance. So conceptually after taking proper measures about the noise and drifts, we can construct a model which is persistent to all the changes it faces. Stream analytics also required exploring the different clustering techniques which have a wide number of applications. We have presented all the empirical analysis carried out on classification and clustering techniques in a tool called MOA.