A Survey on Issues of Data Stream Mining in Classification

As Data Stream Mining is trending topic for Research nowadays and more users increases day by day with online stuff, the size of big data is also getting larger. In traditional data mining extracting knowledge is done mostly using offline phase. While in data stream, Extracting data is from the continuous arriving data or we can say from the online streams. Due to continuously arriving data, it cannot be stored in the memory for processing permanently. So examining of data as fast as possible is important. In this paper we would be interested to discuss about the data stream mining and the issues of stream classification, like Single scan, Load shedding, Memory Space, Class imbalance problem, Concept drift, and possible ways to solve those issues.