Sequence Forecast Algorithm Based on Nonlinear Regression Technique for Stream Data
Source: Kurukshetra University
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.