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Mining regional co-location patterns with kNNG

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Abstract

Spatial co-location pattern mining discovers the subsets of features of which the events are frequently located together in geographic space. The current research on this topic adopts a distance threshold that has limitations in spatial data sets with various magnitudes of neighborhood distances, especially for mining of regional co-location patterns. In this paper, we propose a hierarchical co-location mining framework accounting for both variety of neighborhood distances and spatial heterogeneity. By adopting k-nearest neighbor graph (kNNG) instead of distance threshold, we propose “distance variation coefficient” as a new measure to drive the mining operations and determine an individual neighborhood relationship graph for each region. The proposed mining algorithm outputs a set of regions with each of them an individual set of regional co-location patterns. The experimental results on both synthetic and real world data sets show that our framework is effective to discover these regional co-location patterns.

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Notes

  1. As a direction of our future study we may consider the continuous type of data sets.

References

  • Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. In Proceedings of the 20th international conference on very large data bases, 12–15 Sept. (pp. 487–499). Santiago de Chile, Chile.

  • Arge, L., Procopiuc, O., Ramaswamy, S., Suel, T., Vitter, J.S. (1998). Scalable sweeping-based spatial join. In Proceedings of the 24th international conference on very large data bases, 24–27 Aug. (pp. 570–581). New York, USA.

  • Banerjee, A., & Ghosh, J. (2006). Scalable clustering algorithms with balancing constraints. Data Mining and Knowledge Discovery, 13(3), 365–395.

    Article  MathSciNet  Google Scholar 

  • Brito, M.R., Chavez, E.L., Quiroz, A.J., Yukich, J.E. (1997). Connectivity of the mutual k-nearest-neighbor graph in clustering and outlier detection. Statistics and Probability Letters, 35(1), 33–42.

    Article  MATH  MathSciNet  Google Scholar 

  • Celik, M., Kang, J.M., Shekhar, S. (2007). Zonal co-location pattern discovery with dynamic parameters. In Proceedings of IEEE international conference on data mining, 28–31 Oct. (pp. 433–438). Omaha, USA.

  • Celik, M., Shekhar, S., Rogers, J.P., Shine, J.A., Yoo, J.S. (2006). Mixed-drove spatio-temporal co-occurrence pattern mining: A summary of results. In Proceedings of the 6th international conference on data mining, 18–22 Dec. (pp. 119–128). Hong Kong, China.

  • Digital Chart of the World. (2013). http://www.princeton.edu/~geolib/gis/dcw.html. Accessed 05 Oct 2013.

  • Ding, C., & He, X. (2004). K-nearest-neighbor consistency in data clustering: Incorporating local information into global optimization. In Proceedings of the ACM symposium on applied computing, 14–17 March (pp. 584–589). Nicosia, Cyprus.

  • Eick, C.F., Parmar, R., Ding, W., Stepinski, T.F., Nicot, J.P. (2008). Finding regional co-location patterns for sets of continuous variables in spatial datasets. In Proceedings of the 16th ACM SIGSPATIAL international conference on advances in geographic information systems, 5–7 Nov. (pp. 1–110). Irvine, USA.

  • Friedman, J.H., Bentley, J.L., Finkel, R.A. (1977). An algorithm for finding best matches in logarithmic expected time. ACM Transactions on Mathematical Software, 3, 290–226.

    Google Scholar 

  • Ge, R., Ester, M., Jin, W., Davidson, I. (2007). Constraint-driven clustering. In Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining, 12–15 Aug. (pp. 320–329). San Jose, USA.

  • Han, E.H., Karypis, G., Kumar, V. (1999). Chameleon: hierarchical clustering using dynamic modeling. IEEE Computer, 32(8), 68–75.

    Article  Google Scholar 

  • Huang, Y., Pei, J., Xiong, H. (2006). Mining co-location patterns with rare events from spatial data sets. GeoInformatica, 10(3), 239–260.

    Article  Google Scholar 

  • Huang, Y., Shekhar, S., Xiong, H. (2004). Discovering colocation patterns from spatial datasets: a general approach. IEEE Transactions on Knowledge and Data Engineering, 16(12), 1472–1485.

    Article  Google Scholar 

  • Huang, Y., Zhang, P., Zhang, C. (2008). On the relationships between clustering and spatial co-location pattern mining. International Journal on Artificial Intelligence Tools, 17(1), 55–70.

    Article  Google Scholar 

  • Jin, R., Goswami, A., Agrawal, G. (2006). Fast and exact out-of-core and distributed k-means clustering. Knowledge and Information Systems, 10(1), 17–40.

    Article  Google Scholar 

  • Lin, Z., & Lim, S.J. (2009). Optimal candidate generation in spatial co-location mining. In Proceedings of the 2009 ACM symposium on applied computing, 9–12 March (pp. 1441–1445). Hawaii, USA.

  • Miller, H.J., & Han, J. (2009). Geographic data mining and knowledge discovery. New York: CRC Press.

    Google Scholar 

  • Müller, E., Günnemann, S., Färber, I., Seidl, T. (2012). Discovering multiple clustering solutions: Grouping objects in different views of the data. In Proceeds of the 28th international conference on data engineering, 1–5 Apr. (pp. 1207–1210). Washington DC, USA.

  • Munro, R., Chawla, S., Sun, P. (2003). Complex spatial relationships. In Proceedings of the 3rd IEEE international conference on data mining, 19–22 Dec. (pp. 227–234). Melbourne, USA.

  • Park, K., Shen, C., Hao, Z., Kim, J. (2011). Efficiently learning a distance metric for large margin nearest neighbor classification. In Proceedings of the 25th AAAI conference on artificial intelligence, 7–11 Aug. (pp. 453–458). San Francisco, USA.

  • Qian, F., He, Q., Chiew, K., He, J. (2012). Spatial co-location pattern discovery without thresholds. Knowledge and Information Systems, 33(2), 419–445.

    Article  Google Scholar 

  • Qian, F., He, Q., He, J. (2009a). Mining spread patterns of spatio-temporal co-occurrences over zones. In Proceedings of the international conference on computational science and its applications, 29 June–2 July (pp. 686–701). Seoul, Korea.

  • Qian, F., Yin, L., He, Q., He, J. (2009b). Mining spatio-temporal co-location patterns with weighted sliding window. In Proceedings of IEEE international conference on intelligent computing and intelligent systems, 20–22 Nov. (pp. 181–185). Shanghai, China.

  • Samet, H. (1990). The design and analysis of spatial data structures. Boston: Addison Wesley.

    Google Scholar 

  • Shaw, B., Huang, B.C., Jebara, T. (2011). Learning a distance metric from a network. In Proceeds of the 25th conference on neural information processing systems, 12–14 Dec. (pp. 1899–1907). Granada, Spain.

  • Shekhar, S., & Huang, Y. (2001). Discovering spatial co-location patterns: A summary of results. In Proceedings of the 7th international symposium on spatial and temporal databases, 12–15 July (pp. 236–256). Redondo Beach, USA.

  • Sheng, C., Hsu, W., Lee, M.L., Tung, A.K.H. (2008). Discovering spatial interaction patterns. In Proceedings of the 13th international conference on database systems for advanced applications, 19–21 March (pp. 95–109). New Delhi, India.

  • Slonim, N., Atwal, G.S., Tkačik, G., Bialek, W. (2005). Information-based clustering. Proceedings of the National Academy of Sciences of the United States of America, 102(51), 18297–18302.

    Article  MATH  MathSciNet  Google Scholar 

  • Wang, J. (2006). Spatial analysis. Beijing: Science Press.

    Google Scholar 

  • Wang, L., Zhou, L., Lu, J., Yip, J. (2009). An order-clique-based approach for mining maximal co-locations. Information Sciences, 179(19), 3370–3382.

    Article  MATH  Google Scholar 

  • Wu, X., Kumar, V., Quinlan, J.R., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G., Ng, A., Liu, B., Yu, P., Zhou, Z., Steinbach, M., Hand, D., Steinberg, D. (2008). Top 10 algorithms in data mining. Knowledge and Information Systems, 14(1), 1–37.

    Article  Google Scholar 

  • Xiao, X., Xie, X., Luo, Q., Ma, W.Y. (2008). Density based co-location pattern discovery. In Proceedings of the 16th ACM SIGSPATIAL international conference on advances in geographic information systems, 5–7 Nov. (pp. 1–10). Irvine, USA.

  • Xiong, H., Shekhar, S., Huang, Y., Kumar, V., Ma, X., Yoo, J.S. (2004). A framework for discovering co-location patterns in data sets with extended spatial objects. In Proceedings of the fourth SIAM international conference on data mining, 22–24 Apr. (vol. 89, p. 78). Lake Buena, USA.

  • Yoo, J.S., & Shekhar, S. (2006). A joinless approach for mining spatial colocation patterns. IEEE Transactions on Knowledge and Data Engineering, 18(10), 1323–1337.

    Article  Google Scholar 

  • Yoo, J.S., Shekhar, S., Kim, S., Celik, M. (2006). Discovery of co-evolving spatial event sets. In Proceedings of the 6th SIAM international conference on data mining, 20–22 Nov. (pp. 306–315). Bethesda, USA.

  • Yoo, J.S., & Bow, M. (2012). Mining spatial colocation patterns: a different framework. Data Mining and Knowledge Discovery, 24(1), 1384–5810.

    Article  MathSciNet  Google Scholar 

  • Zhang, X., Mamoulis, N., Cheung, D.W., Shou, Y. (2004). Fast mining of spatial collocations. In Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining, 22–25 Aug. (pp. 384–393). Seattle, USA.

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Acknowledgements

This work is partly supported by National Key Technologies R&D Program of China under Grant No. 2011BAD21B02, in which Chiew’s work is partly supported by National Natural Science Foundation of China under Grant No. 61272303.

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Authors and Affiliations

  1. Hangzhou R&D Center, NetEase Inc., No.599, Wangshang Road, Bingjiang District, Hangzhou, Zhejiang Province, People’s Republic of China

    Feng Qian

  2. School of Engineering, Tan Tao University, Duc Hoa District, Long An Province, Vietnam

    Kevin Chiew

  3. College of Computer Science and Technology, Zhejiang University, Hangzhou, People’s Republic of China

    Qinming He

  4. School of Computing, National University of Singapore, Singapore, Singapore

    Hao Huang

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  1. Feng Qian
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  2. Kevin Chiew
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  3. Qinming He
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  4. Hao Huang
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Correspondence to Feng Qian.

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Qian, F., Chiew, K., He, Q. et al. Mining regional co-location patterns with kNNG. J Intell Inf Syst 42, 485–505 (2014). https://doi.org/10.1007/s10844-013-0280-5

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  • DOI: https://doi.org/10.1007/s10844-013-0280-5

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