Abstract
This paper proposes User’s Location Prediction System using the Filtering with Correlation Coefficients Weight. This system heterogeneous data occurred during the process of collecting context information into homogeneous data, and improves the accuracy of clustering needed for location-awareness. Also, it applies correlation coefficients weight to extract features and predicts user’s location through ARIMA time-series analysis. For the evaluation of the proposed method a test bed is constructed, tested and compared with the method without weighting.
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References
Dev, A.K.: Understanding and Using Context. Personal and Ubiquitous Computing Journal 5(1), 4–7 (2001)
You, C.W., Chen, Y.C., Chu, H.H., Huang, P., Chiang, J.R., Lau, S.Y.: Sensor-enhanced mobility prediction for energy efficient localization. In: Proc. Sensor and Ad Hoc Communications and Networks (SECON 2006), vol. 2, pp. 565–574 (2006)
Mayrhofer, R.: An Architecture for Context Prediction. PhD thesis, Johannes Kepeler University of Linz, Altenbergstrasse 69, 4040 Linz, Austria (2004)
Sigg, S.: Development of a novel context prediction algorithm and analysis. Kassel University Press, GmbH (2008)
Choi, C.-Y., Lim, J.-H.: Location Prediction of Time Series Analysis using Correlation Coefficient Weight. Korean Society for Internet Information 12(2), 73–76 (2009)
Negri, S., Belanche, L.A.: Heterogeneous kohonen networks. In: Mira, J., Prieto, A.G. (eds.) IWANN 2001. LNCS, vol. 2084, pp. 243–252. Springer, Heidelberg (2001)
Mayrhofer, R., Radi, H., Ferscha, A.: Feature extraction in wireless personal and local area networks. In: Agha, K.A., Omidyar, C.G. (eds.) TheProceedings of The Fifth IFIP-TC6 International Conference on Mobile and Wireless Communications Networks (MWCN 2003), pp. 195–198 (2003)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publisher, San Francisco (2001)
Tan, P.-N., Steinbach, M., Kumar, V.: Introduction to data mining. Pearson Addison-Wesley, London (2006)
Everitt, B., Landau, S., Leese, M.: Cluster Analysis. Edward Arnold, London (2001)
Kohonen, T.: Self-Organizing Maps. Springer, Berlin (1997)
Lee, J.-S., Kang, M.-K.: A Clustering Algorithm Using the Ordered Weight of Self-Organizing Feature Maps. In: Korean Operations Research and Management Science Society, pp. 41–51 (2006)
ki, Y.-H.: Time Series Prediction. In: Hyung Seul Publish (2001)
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Im, KM., Choi, CY., Lim, JH. (2011). User’s Location Prediction System Using the Filtering with Correlation Coefficients Weight. In: Park, J.J., Yang, L.T., Lee, C. (eds) Future Information Technology. Communications in Computer and Information Science, vol 185. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22309-9_51
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DOI: https://doi.org/10.1007/978-3-642-22309-9_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22308-2
Online ISBN: 978-3-642-22309-9
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