An Imputation Technique for Missing Context Data Based on Spatial-temporal and Association Rule Mining

Wang Yu-Xiang; Qiao  Xiu-Quan; Li Xiao-Feng; Meng Luo-Ming

doi:10.3724/SP.J.1146.2010.00035

Volume 32 Issue 12

Jan. 2011

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Article Navigation > Journal of Electronics & Information Technology > 2010 > 32(12): 2913-2918

Wang Yu-Xiang, Qiao Xiu-Quan, Li Xiao-Feng, Meng Luo-Ming. An Imputation Technique for Missing Context Data Based on Spatial-temporal and Association Rule Mining[J]. Journal of Electronics & Information Technology, 2010, 32(12): 2913-2918. doi: 10.3724/SP.J.1146.2010.00035

Citation:

Wang Yu-Xiang, Qiao Xiu-Quan, Li Xiao-Feng, Meng Luo-Ming. An Imputation Technique for Missing Context Data Based on Spatial-temporal and Association Rule Mining[J]. Journal of Electronics & Information Technology, 2010, 32(12): 2913-2918. doi: 10.3724/SP.J.1146.2010.00035

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An Imputation Technique for Missing Context Data Based on Spatial-temporal and Association Rule Mining

doi: 10.3724/SP.J.1146.2010.00035 cstr: 32379.14.SP.J.1146.2010.00035

Received Date: 2010-01-12
Rev Recd Date: 2010-07-05
Publish Date: 2010-12-19

Abstract

Abstract

The context data missing is an inevitable problem of context information processing mechanism, the imputation technique of missing data also is a research hotspot in data mining. However, the existing imputation technique of missing data is not suitable for the flow data form of context information that does not make full use of data relevance between every collecting sensor. Moreover, that does not take spatial-temporal relationship into account. In order to conquer the shortcomings and deficiencies of the existing imputation technique of missing data, this paper proposes an imputation technique for context data missing based on Spatial-Temporal and Association Rule Mining (STARM) to perform spatiality and time series analysis on sensor data, and generate strong association rules to interpolate missing data. Finally, the simulation experiment verifies the rationality and efficiency of STARM through temperature sensor data acquisition. Experiments show that the algorithm is of high accuracy for the imputation of context data missing, such as Simple Linear Regression (SLR) algorithm and the EM algorithm. In addition, which is smaller time and space overhead and can guarantee Quality of Service (QoS) of real-time applications.
- Information processing,
- Spatial-temporal relation,
- Association rule mining,
- Imputation for missing context data,
- Root Mean Square Error (RMSE)

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References(1)

References

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