Prediction of Frequent Items to One Dimensional Stream Data
Duck Jin Chai, Eun Hee Kim, Long Jin, Bu-Hyun Hwang, Keun Ho Ryu · 2007
Data mining in the stream data handles quality and data analysis using extremely large and infinite amount of data and disk or memory with limited volume. 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 one dimensional stream data like the time series data. By establishing the regression model from the stream data, it may be used as a prediction model to uncertain items. The proposing way will exhibit its effectiveness through experiment in stream data.