Sequence Forecast Algorithm Based on Nonlinear Regression Technique for Stream Data

Data mining is the process of extracting knowledge structures from continuous, rapid and extremely large stream data which handles quality and data analysis. In such traditional transaction environment it is impossible to perform frequent items mining because it requires analyzing which item is a frequent one to continuously incoming stream data and which is probable to become a frequent item. This paper proposes a way to predict frequent items using regression model to the continuously incoming real time stream data. By establishing the regression model from the stream data, it may be used as a prediction model to uncertain items. After gathering real-time stream data through sliding window, the proposed algorithm computes support for appointed sequence and describes non linear equation to forecast sequence trends in the future.