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Frequent Items Mining based on Regression Model in Data Streams

스트림 데이터에서 회귀분석에 기반한 빈발항목 예측

  • Published : 2009.01.28

Abstract

Recently, the data model in stream data environment has massive, continuous, and infinity properties. However the stream data processing like query process or data analysis is conducted using a limited capacity of disk or memory. In these environment, the traditional frequent pattern discovery on transaction database can be performed because it is difficult to manage the information continuously whether a continuous stream data is the frequent item or not. In this paper, we propose the method which we are able to predict the frequent items using the regression model on continuous stream data environment. We can use as a prediction model on indefinite items by constructing the regression model on stream data. We will show that the proposed method is able to be efficiently used on stream data environment through a variety of experiments.

Keywords

Stream Data;Frequent Item;Regression Models;Prediction

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