UMine: Study on Prevalent Co-locations Mining from Uncertain Data Sets
We can collect a large amount of spatial data by utilizing sensor positioning technology and wearable devices. However, most of the acquired data are uncertain because of the gaps in data collection or to maintain subject privacy. Thus, we investigate co-location pattern mining problem in the context of uncertain data. The prevalent co-location pattern under uncertain environments has two different definitions. The first definition, referred as the expected prevalent co-location, employs the expected interest degree of co-location to measure whether this pattern is frequent. The second definition, referred as the probabilistic prevalent co-location, uses the probabilistic formulations to measure frequency. Here a novel system called UMine is proposed to compare this two different definitions with a user-friendly interface. The core of a system such as this is the mining algorithm, and UMine is integrated with the expected mining method, probabilistic mining method, and approximate mining method. In this paper, the system is introduced in detail, and the comparison between these two types of definitions is implemented. The experimental results show that the difference between these two definitions’ result sets changes as the threshold changes. By flexibly adjusting the parameters, users can observe interesting patterns in the data. In addition, the demonstration provides data generation and preprocessing function while showing its practicality for either real-world or synthetic data sets. The study can also provide support for the further uncertain Co-location patterns mining research.
KeywordsSpatial co-location patterns Uncertain data Possible worlds Visualization
This paper was supported by the Research Foundation of Educational Department of Yunnan Province (No. 2016ZZX304).
- 1.Morimoto, Y.: Mining frequent neighboring class sets in spatial databases. In: Proceedings of SIGKDD, pp. 353–358 (2001)Google Scholar
- 3.Yoo, J.S., Shekhar, S.: A Partial Join approach for mining co-location patterns. In: Proceedings of the 12th Annual ACM International Workshop on Geographic Information Systems, pp. 241–249. ACM Press (2004)Google Scholar
- 4.Yoo, J.S., Shekhar, S., Celik, M.: 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), pp. 813–816. IEEE Press (2005)Google Scholar
- 7.Jiang, Y., Wang, L., Lu, Y., et al.: Discovering both positive and negative co-location rules from spatial data sets. In: International Conference on Software Engineering and Data Mining, pp. 398–403. IEEE (2010)Google Scholar
- 10.Ye, L.U., Wang, L., et al.: Spatial co-location patterns mining over uncertain data based on possible worlds. J. Comput. Res. Dev. 47(Supp l.), 215–221 (2010). (in Chinese with English abstract)Google Scholar
- 11.Wang, L., Wu, P., Chen, H.: Finding probabilistic prevalent colocations in spatially uncertain data sets. TKDE 25(4), 790–804 (2013)Google Scholar
- 13.Wang, L., Lu, Y., Chen, H., Xiao, Q.: Prefix-tree-based spatial co-location patterns mining algorithms. J. Comput. Res. Dev. 47(Suppl.), 370–377 (2010). (in Chinese with English abstract)Google Scholar
- 14.Wang, L., Bao, Y., Lu, J., et al.: A web-based visual spatial co-location patterns’ mining prototype system (SCPMiner). In: International Conference on Cyberworlds, pp. 675–681. IEEE (2009)Google Scholar
- 15.Green, T.J., Tannen, V.: Models for incomplete and probabilistic information. In: IEEE Data Engineering Bulletin, pp. 278–296 (2006)Google Scholar