Advertisement

Co-location Detector: A System to Find Interesting Spatial Co-locating Relationships

  • Xuguang Bao
  • Lizhen Wang
  • Qing Xiao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9932)

Abstract

Data Mining develops from original transactional data to current spatial data, this trend indicates that the data is getting more complex and the mining algorithms require better performances. Co-location patterns describe the subsets of features whose instances are prevalently located together in geographic space. Co-location mining algorithms are to find prevalent (interesting) co-location patterns with some thresholds given by the user. Co-location Detector is a system which improves the join-less algorithm and optimizes some details, it owns friendly interactive interface and good operational experiences, visualizes the co-location patterns for the user to process the next decision, besides, the user can change his input parameters to compare the results in order to mine more valuable information.

Keywords

Co-location Join-less User decision Visualize 

Notes

Acknowledgements

This work was supported in part by grants (No. 61472346, No. 61262069) from the National Natural Science Foundation of China and in part by a grant (No. 2016FA026, No. 2015FB149, and No. 2015FB114) from the Science Foundation of Yunnan Province.

References

  1. 1.
    Huang, Y., Shekhar, S., Xiong, H.: Discovering co-location patterns from spatial data sets: a general approach. IEEE Trans. Knowl. Data Eng. (TKDE) 16(12), 1472–1485 (2004)CrossRefGoogle Scholar
  2. 2.
    Agrawal, R., Skrikant, R.: Fast algorithms for mining association rules. In: The 20th International Conference on Very Large Data Bases, pp. 487–499 (1994)Google Scholar
  3. 3.
    Yoo, J.S., Shekhar, S.: A joinless approach for mining spatial collocation patterns. IEEE Trans. Knowl. Data Eng. (TKDE) 18(10), 1323–1337 (2006)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.School of Information Science and EngineeringYunnan UniversityKunmingChina

Personalised recommendations