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基于改进列计算的空间并置模式挖掘方法.
- Source :
-
Application Research of Computers / Jisuanji Yingyong Yanjiu . May2024, Vol. 41 Issue 5, p1374-1380. 7p. - Publication Year :
- 2024
-
Abstract
- Spatial co-location pattern mining aims to discover the association between spatial features and has been an important research direction in spatial data mining. Spatial co-location pattern mining method based on column calculation (CPM-Col algorithm) avoids the most time-consuming operation of generating table instances and directly searches for participating instances. This method has become one of the most efficient approaches. However, backtracking search for participating instances remains a bottleneck, especially in dense datasets and long pattern mining. To accelerate the search for participating instances, this paper proposed two improvements to the CPM-Col algorithm with less extra computations. Firstly, the row instances found by CPM-Col algorithm were stored as partial table instances, for avoiding backtracking calculations of many instances. Secondly, after successfully finding a row instance, some instances of the first feature were obtained by the sub-clique reaction of the row instance. Based on these improvements, this paper proposed a co-location pattern mining method based on improved column calculation (CPM-iCol algorithm) and discussed complexity, correctness, and completeness. Experiments were conducted on synthetic and real datasets. Comparing to a classical algorithm join-less and CPM-Col, the CPM-iCol algorithm significantly reduces mining time and backtracking times. The results show that the proposed algorithm has better performance and scalability than CPM-Col algorithm, especially in dense datasets. [ABSTRACT FROM AUTHOR]
Details
- Language :
- Chinese
- ISSN :
- 10013695
- Volume :
- 41
- Issue :
- 5
- Database :
- Academic Search Index
- Journal :
- Application Research of Computers / Jisuanji Yingyong Yanjiu
- Publication Type :
- Academic Journal
- Accession number :
- 177254395
- Full Text :
- https://doi.org/10.19734/j.issn.1001-3695.2023.09.0448