Context-aware data mining framework for wireless medical application

Abstract

Data mining, which aims at extracting interesting information from large collections of data, has been widely used as an effective decision making tool. Mining the datasets in the presence of context factors may improve performance and efficacy of data mining by identifying the unknown factors, which are not easily detectable in the process of generating an expected outcome. This paper proposes a Context-aware data mining framework, by which contexts will be automatically captured to maximize the adaptive capacity of data mining. Context could consist of any circumstantial factors of the user and domain that may affect the data mining process. The factors that may affect the mining behavior are delineated and how each factor affects the behavior is discussed. It is also observed that a medical application of the model in wireless devices offers the advantages of Context-aware data mining. A Context-aware data mining framework is quantified through a partial implementation that would be used to test the behavior of the mining system under varied context factors. The results obtained from the implementation process are elucidated on how the prediction output or the behavior of the system changes from the similar set of inputs in view of different context factors. © Springer-Verlag Berlin Heidelberg 2003.