Abstract
Uncertain data are inherent in many applications, and are usually described by precise probabilities. However, it is difficult to obtain precise probabilities over uncertain data in applications. This paper studies the problem of mining co-locations from spatially uncertain data with probability intervals. Firstly, it defines the possible world model with probability intervals, and proves that probability intervals of all possible worlds are feasible. Secondly, based on the feasible probability interval, it converts the probability intervals of possible worlds into point probabilities. Further, it defines the related concepts of probabilistic prevalent co-locations. Thirdly, it gives two lemmas for optimizing the computation of prevalence point probability of a candidate co-location. Further, it proves the closure property of prevalence point probability. Finally, the experiments on synthetic and real data sets show that the algorithms are effective and significant.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Wang, L., Zhou, L., Chen, H., et al.: The principle and applications of data warehouses and data mining, 2nd edn. Science press, Beijing (2009)
Klir, G.J., Watson, T.J.: Uncertainty and information measures for imprecise probabilities: an overview. In: The First International Symposium on Imprecise Probabilities and Their Applications (ISIPTA 1999), Ghent, Belgium, pp. 234–240 (1999)
Huang, Y., Shekhar, S., Xiong, H.: Discovering co-location patterns from spatial data sets: a general approach. IEEE Transactions on Knowledge and Data Engineering (TKDE) 16(12), 1472–1485 (2004)
Yoo, J.S., Shekhar, S.: A join-less approach for co-location pattern mining: a summary of results. IEEE Transactions on Knowledge and Data Engineering (TKDE) 18(10), 1323–1337 (2006)
Wang, L., Bao, Y., Lu, J., Yip, J.: A new join-less approach for co-location pattern mining. In: The 8th IEEE International Conference on Computer and Information Technology (CIT 2008), pp. 197–202. IEEE Press, New York (2008)
Wang, L., Zhou, L., Lu, J., Yip, J.: An order-clique-based approach for mining maximal co-locations. Information Sciences 179(19), 3370–3382 (2009)
Ouyang, Z., Wang, L., Chen, H.: Mining spatial co-location patterns for fuzzy objects. Chinese Journal of Computers 34(10), 1947–1955 (2011)
Huang, Y., Pei, J., Xiong, H.: Mining co-location patterns with rare events from spatial data sets. Geoinformatica 10(3), 239–260 (2006)
Feng, L., Wang, L., Gao, S.: A new approach of mining co-location patterns in spatial datasets with rare features. Journal of Nanjing University (Natural Sciences) 48(1), 99–107 (2012)
Lu, Y., Wang, L., Zhang, X.: Mining frequent co-location patterns from uncertain data. Journal of Frontiers of Computer Science and Technology 3(6), 656–664 (2009)
Lu, Y., Wang, L., Chen, H., et al.: Spatial co-location patterns mining over uncertain data based on possible worlds. Journal of Computer Research and Development, 47 (suppl.), 215–221 (2010)
Wang, L., Wu, P., Chen, H.: Finding probabilistic prevalent co-locations in spatially uncertain data sets. IEEE Transactions on Knowledge and Data Engineering (TKDE) 25(4), 790–804 (2013)
Abellan, J., Moral, S.: Maximum of entropy for credal sets. International Journal of Uncertainty, Fuzziness and Knowledge Based Systems 11(5), 587–597 (2003)
He, D., Zhou, R.: Study on methods of decision-making under interval probability. Journal of Systems and Management 19(2), 210–214 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, L., Guan, P., Chen, H., Xiao, Q. (2013). Mining Co-locations from Spatially Uncertain Data with Probability Intervals. In: Gao, Y., et al. Web-Age Information Management. WAIM 2013. Lecture Notes in Computer Science, vol 7901. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39527-7_30
Download citation
DOI: https://doi.org/10.1007/978-3-642-39527-7_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39526-0
Online ISBN: 978-3-642-39527-7
eBook Packages: Computer ScienceComputer Science (R0)