Skip to main content
Springer Nature Link
Log in

Mining high influence co-location patterns from instances with attributes

  • Special Issue
  • Published:
Evolutionary Intelligence Aims and scope Submit manuscript

Abstract

A spatial co-location pattern describes coexistence of spatial features whose instances frequently appear together in geographic space. Numerous studies have been proposed to discover interesting co-location patterns from spatial data sets, but most of them only use the location information of instances. As a result, they cannot adequately reflect the influence between instances. In this paper, we take additional attributes of instances into account in the process of co-location pattern mining, and propose a new approach for discovering the high influence co-location patterns. In our approach, we consider the spatial neighboring relationships and the similarity of instances simultaneously, and utilize the information entropy approach to measure the influence of any instance exerting on its neighbors and the influence of any feature in a co-location pattern. Then, an influence index for measuring the interestingness of a co-location pattern is proposed and we prove the influence index measure satisfies the downward closure property that can be used for pruning the search space, and thus an efficient high influence co-location pattern mining algorithm is designed. At last, extensive experiments are conducted on synthetic and real spatial data sets. Experimental results reveal the effectiveness and efficiency of our method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Notes

  1. National bureau of statistics of China: http://data.stats.gov.cn/search.htm. Accessed 20 Jan 2019.

References

  1. Koperski K, Han J (1995) Discovery of spatial association rules in geographic information databases. In: Proceedings of international symposium on large spatial data bases, Portland, ME, pp 47–66

  2. Shekhar S, Huang Y (2001) Discovering spatial co-location patterns: a summary of results. In: Proceedings of the 7th international symposium on advances in spatial and temporal database (SSTD). Springer, Berlin, pp 236–240

    Chapter  Google Scholar 

  3. Huang Y, Shekhar S, Xiong H (2004) Discovering colocation patterns from spatial data sets: a general approach. IEEE Trans Knowl Data Eng (TKDE) 16(12):1472–1485

    Article  Google Scholar 

  4. Yoo JS, Shekhar S (2004) A partial join approach for mining co-location patterns. In: Proceedings of the 12th annual ACM international workshop on geographic information systems. ACM Press, pp 241–249

  5. Yoo JS, Shekhar S, Celik M (2005) A join-less approach for co-location pattern mining: a summary of result. In: Proceedings of the 5th IEEE international conference on data mining (ICDM). IEEE Press, pp 813–816

  6. Yoo JS, Shekhar S (2006) A join-less approach for mining spatial co-location pattern. IEEE Trans Knowl Data Eng (TKDE) 18(10):1323–1337

    Article  Google Scholar 

  7. Wang L, Bao Y, Lu Z (2009) Efficient discovery of spatial co-location patterns using the iCPI-tree. Open Inf Syst J 3(1):69–80

    Google Scholar 

  8. Wang L, Bao Y, Lu J et al. (2008) A new join-less approach for co-location pattern mining. In: Proceedings of the IEEE 8th international conference on computer and information technology (CIT2008). The IEEE Computer Society Press, Piscataway, NJ, pp 197–202

  9. Wang L, Zhou L, Lu J et al (2009) An order-clique-based approach for mining maximal co-locations. Inf Sci 179(19):3370–3382

    Article  Google Scholar 

  10. Bembenik R, Jozwicki W, Protaziuk G (2017) Methods for mining co-location patterns with extended spatial objects. Int J Appl Math Comput Sci 27:681–695

    Article  MathSciNet  Google Scholar 

  11. Xiong H, Shekhar S, Huang Y, Kumar V, Ma XB, Yoo JS (2008) A framework for discovering co-location patterns in data sets with extended spatial objects. In: Proceedings of the 4th SIAM international conference on data mining, Lake Buena Vista, Florida, USA. ACM Press, California, pp 1–10

  12. Kim SK, Lee JH, Ryu KH (2014) A framework of spatial co-location pattern mining for ubiquitous GIS. Multimed Tools Appl 71:199–218

    Article  Google Scholar 

  13. Li JD, Adilmagambetov A, Jabbar MSM, Zaiane OR, Osornio-Vargas A, Wine O (2016) On discovering co-location patterns in datasets: a case study of pollutants and child cancers. Geoinformatica 20:651–692

    Article  Google Scholar 

  14. Chen L (2017) Spatial high impact co-location pattern mining. Dissertation, Yunnan University (in Chinese)

  15. Agrawal R, Imielinski T, Swami A (1993) Mining association rules between sets of items in large databases. In Proceedings of the 1993 ACM SIGMOD international conference on management of data. ACM, Washington, DC, pp 207–216

  16. Giacometti A, Li DH, Marcel P et al (2014) 20 years of pattern mining: a bibliometric survey. ACM SIGKDD Explor Newsl 15(1):41–50

    Article  Google Scholar 

  17. Bayardo R J (1998) Efficiently mining long patterns from databases. In: Proceedings of 1998 ACM-SIGMOD international conference on management of data. ACM, Seattle, WA, pp 85–93

  18. Pan F, Tung A K H, Cong G et al. (2004) COBBLER: combining column and row enumeration for closed pattern discovery. In: Proceedings of 2004 international conference on scientific and statistical database management, Santorini Island, Greece, pp 21–30

  19. Wang C, Zheng X (2019) Application of improved time series Apriori algorithm by frequent itemsets in association rule data mining based on temporal constraint. Evolutionary intelligence. Springer, Berlin. https://doi.org/10.1007/s12065-019-00234-5

    Book  Google Scholar 

  20. Huang Y, Zhang L, Yu P (2005) Can we apply projection-based frequent pattern mining paradigm to spatial co-location mining? In: Proceedings of the Pacific-Asia conference on the methodologies for knowledge discovery and data mining (PAKDD 2005). Springer, Berlin, pp 719–725

    Chapter  Google Scholar 

  21. Pasquier N, Bastide Y, Taouil R, Lakhal L (1999) Discovering frequent closed itemsets for association rules. In: Proceedings of the 7th international conference on database theory (ICDT’99), Jerusalem, Israel, pp 398–416

    Google Scholar 

  22. Wang L, Han J, Chen H et al (2016) Top-k probabilistic prevalent co-location mining in spatially uncertain data sets. Front Comput Sci 10(3):1–16

    Article  Google Scholar 

  23. Wang L, Chen H, Zhao L et al. (2010) Efficiently mining co-location rules on interval data. In: Proceedings international conference on advanced data mining and applications, ADMA 2010, Springer, Berlin, pp 477–488

    Chapter  Google Scholar 

  24. Ouyang Z, Wang L, Wu P (2017) Spatial co-location pattern discovery from fuzzy objects. Int J Artif Intell Tools 26:2

    Article  Google Scholar 

  25. Lin C, Lan G, Hong T (2012) An incremental mining algorithm for high utility itemsets. Expert Syst Appl 39(8):7173–7180

    Article  Google Scholar 

  26. Chefrour A (2019) Incremental supervised learning: algorithms and applications in pattern recognition. Evol Intell 12:97. https://doi.org/10.1007/s12065-019-00203-y

    Article  Google Scholar 

  27. Chai Z, Liang S (2019) A node-priority based large-scale overlapping community detection using evolutionary multi-objective optimization. Evol Intell. https://doi.org/10.1007/s12065-019-00250-5

    Article  Google Scholar 

  28. Xiang R, Neville J, Rogati M (2010) Modeling relationship strength in online social networks. In: Proceedings of the 19th international world wide web conference (WWW2010), Raleigh, NC, USA, pp 981–990

  29. Sathanur AV, Jandhyala V (2014) An activity-based information-theoretic annotation of social graphs. In: Proceedings of the 2014 ACM conference on web science (WEBSCI 2014), Bloomington, USA, pp 187–191

  30. Peng S, Wang G, Yu S (2013) Mining mechanism of top-k influential nodes based on voting algorithm in mobile social networks. In: Proceedings of the 11th IEEE/IFIP international conference on embedded and ubiquitous computing (EUC 2013), Zhangjiajie, China, pp 2194–2199

  31. Bakshy E, Hofman JM, Mason WA, Watts DJ (2011) Everyone’s an influencer: quantifying influence on twitter. In: Proceedings of the 4th ACM international conference on web search and data mining (WSDM 2011), Hong Kong, China, pp 65–74

  32. Huang J, Cheng X, Shen H, Zhou T, Jin X (2012) Exploring social influence via posterior effect of word-of-mouth recommendations. In: Proceedings of the 5th ACM international conference on web search and data mining (WSDM 2012), Seattle, Washington, USA, pp 573–582

  33. Peng SC, Yang AM, Cao LH, Yu S, Xie DQ (2016) Social influence modeling using information theory in mobile social networks. Inf Sci 379:146–159

    Article  Google Scholar 

  34. Wang L, Chen H (2014) Spatial pattern mining theory and method. Science Press, Beijing (in Chinese)

    Google Scholar 

  35. Baidu Map API (developer interface) (2018) Baidu Map Open Platform. http://lbsyun.baidu.com/. Accessed 22 Apr 2018

  36. Behdad M, French T, Barone L et al (2012) On principal component analysis for high-dimensional XCSR. Evol Intell 5:129. https://doi.org/10.1007/s12065-012-0075-6

    Article  Google Scholar 

Download references

Acknowledgement

This work was supported in part by Grants (Nos. 61966036, 61662086) from the National Natural Science Foundation of China, and in part by the Project of Innovation Research Team of Yunnan Province (No. 2018HC019), and in part by the Research and Innovation Project of Yunnan University (No. 2018216).

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Yunnan University, Kunming, 650504, Yunnan Province, China

    Dianwu Fang, Lizhen Wang & Peizhong Yang

  2. China Mobile Group Anhui Co., Ltd., Lu’an Branch, Lu’an, 237005, Anhui Province, China

    Lan Chen

Authors
  1. Dianwu Fang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Lizhen Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Peizhong Yang
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Lan Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Lizhen Wang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fang, D., Wang, L., Yang, P. et al. Mining high influence co-location patterns from instances with attributes. Evol. Intel. 13, 197–210 (2020). https://doi.org/10.1007/s12065-019-00321-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12065-019-00321-7

Keywords