Abstract
Co-location pattern mining plays an important role in spatial data mining. With the rapid growth of spatial datasets, the usefulness of co-location patterns is strongly limited by the huge amount of the discovered patterns. To overcome this drawback, several statistics-based methods have been proposed to reduce the number of discovered co-location patterns. However, these methods cannot guarantee that their mined patterns are really user-preferred. Therefore, it is crucial to help users discover the co-location patterns they preferred through effective interactions. This paper proposes a newly interactive approach based on SVM, in order to discover user-preferred co-location patterns. First, we presented an originally interactive framework to help the user discover his/her preferred co-location patterns. Then, we designed a filtering algorithm with a small part of patterns as the training set of the SVM model for the provision of each interactive process, by which a high efficiency could be achieved by the optimization of the SVM model. Finally, the system was verified on both the real data sets and the synthetic data sets, accompanying with the 80% prediction accuracy.
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References
Verhein, F., Al-Naymat, G.: Fast mining of complex spatial co-location patterns using GLIMIT. In: Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007), pp. 679–684. IEEE (2007)
Anderson, J.M., et al.: Simulations of VLBI observations of a geodetic satellite providing co-location in space. J. Geodesy 92(9), 1023–1046 (2018). https://doi.org/10.1007/s00190-018-1115-5
Shekhar, S., Huang, Y.: Discovering spatial co-location patterns: A summary of results. In: Jensen, C.S., Schneider, M., Seeger, B., Tsotras, V.J. (eds.) SSTD 2001. LNCS, vol. 2121, pp. 236–256. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-47724-1_13
Huang, Y., Shekhar, S., Xiong, H.: Discovering colocation patterns from spatial data sets: a general approach. IEEE Trans. Knowl. Data Eng. 16(12), 1472–1485 (2004)
Wang, L., et al.: An order-clique-based approach for mining maximal co-locations. Inf. Sci. 179(19), 3370–3382 (2009)
Yoo, J.S., Bow, M.: Mining top-k closed co-location patterns. In: Proceedings of 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services, pp. 100–105 (2011)
Wang, L., et al.: Effective lossless condensed representation and discovery of spatial co-location patterns. Inf. Sci. 436, 197–213 (2018)
Wang, L., Bao, X., Cao, L.: Interactive probabilistic post-mining of user-preferred spatial co-location patterns. In: 2018 IEEE 34th International Conference on Data Engineering (ICDE), pp. 1256–1259. IEEE (2018)
Bao, X., et al.: Knowledge-based interactive postmining of user-preferred co-location patterns using ontologies. IEEE Trans. Cybern. 1–14 (2021)
Haddoud, M., Mokhtari, A., Lecroq, T., Abdeddaïm, S.: Combining supervised term-weighting metrics for SVM text classification with extended term representation. Knowl. Inf. Syst. 49(3), 909–931 (2016). https://doi.org/10.1007/s10115-016-0924-1
Bertoni, A., Folgieri, R., Valentini, G.: Bio-molecular cancer prediction with random subspace ensembles of support vector machines. Neurocomputing 63, 535–539 (2005)
Inglada, J.: Automatic recognition of man-made objects in high resolution optical remote sensing images by SVM classification of geometric image features. ISPRS J. Photogrammetry Remote Sens. 62, 236–248 (2007)
Yoo, J.S., Shekhar, S., Smith, J., Kumquat, J.P.: A partial join approach for mining co-location patterns. In: Proceedings of ACM IWGIS, pp. 241–249 (2004)
Yoo, J.S., Shekhar, S.: A joinless approach for mining spatial colocation patterns. IEEE Trans. Knowl. Data Eng. 18(10), 1323–1337 (2006)
Bao, X., Wang, L.: Discovering interesting co-location patterns interactively using ontologies. In: Bao, Z., Trajcevski, G., Chang, L., Hua, W. (eds.) DASFAA 2017. LNCS, vol. 10179, pp. 75–89. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-55705-2_6
Yang, K., et al.: Towards factorized SVM with Gaussian Kernels over normalized data. In: 2020 IEEE 36th International Conference on Data Engineering (ICDE). IEEE (2020)
Bao, X., Wang, L., Xiao, Q.: OICPM: an interactive system to find interesting co-location patterns using ontologies. In: Chen, L., Jensen, C.S., Shahabi, C., Yang, X., Lian, X. (eds.) APWeb-WAIM 2017. LNCS, vol. 10367, pp. 329–332. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-63564-4_29
Acknowledgements
This work was supported in part by grants (No. U1811264, No. U1711263, No. 61966009, No. 62006057, 61762027) from the National Natural Science Foundation of China, in part by grants (No. 2018GXNSFDA281045, No. 2019GXNSFBA245059) from the Natural Science Foundation of Guangxi Province, and in parts by grants (No. AD19245011) from the Key Research and Development Program of Guangxi Province.
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Zhang, Y., Bao, X., Chang, L., Gu, T. (2022). Interactive Mining of User-Preferred Co-location Patterns Based on SVM. In: Shi, Z., Zucker, JD., An, B. (eds) Intelligent Information Processing XI. IIP 2022. IFIP Advances in Information and Communication Technology, vol 643. Springer, Cham. https://doi.org/10.1007/978-3-031-03948-5_8
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