不均匀模糊空间对象的分层次co-location模式挖掘方法

doi:10.11772/j.issn.1001-9081.2016.11.3113

计算机应用 ›› 2016, Vol. 36 ›› Issue (11): 3113-3117.DOI: 10.11772/j.issn.1001-9081.2016.11.3113

不均匀模糊空间对象的分层次co-location模式挖掘方法

俞庆英^1,2, 罗永龙^1,2, 吴倩¹, 陈传明¹

1. 安徽师范大学数学计算机科学学院, 安徽芜湖 241003;
2. 安徽师范大学国土资源与旅游学院, 安徽芜湖 241003

收稿日期:2016-05-11 修回日期:2016-06-22 出版日期:2016-11-10 发布日期:2016-11-12
通讯作者: 罗永龙
作者简介:俞庆英(1980-),女,安徽黄山人,讲师,博士研究生,CCF会员,主要研究方向:空间数据处理、信息安全;罗永龙(1972-),男,安徽太湖人,教授,博士生导师,博士,CCF会员,主要研究方向:信息安全、空间数据处理;吴倩(1994-),女,安徽六安人,主要研究方向:数据挖掘;陈传明(1981-),男,安徽六安人,副教授,博士研究生,CCF会员,主要研究方向:数据挖掘、智能计算。
基金资助:
国家自然科学基金资助项目（61370050，61572036）；安徽省自然科学基金资助项目（1508085QF134）；安徽师范大学创新基金资助项目（2016XJJ074）。

Hierarchical co-location pattern mining approach of unevenly distributed fuzzy spatial objects

YU Qingying^1,2, LUO Yonglong^1,2, WU Qian¹, CHEN Chuanming¹

1. School of Mathematics and Computer Science, Anhui Normal University, Wuhu Anhui 241003, China;
2. School of Territorial Resources and Tourism, Anhui Normal University, Wuhu Anhui 241003, China

Received:2016-05-11 Revised:2016-06-22 Online:2016-11-10 Published:2016-11-12
Supported by:
This work is partially supported by the National Natual Science Foundation of China (61370050, 61572036), the Natual Science Foundation of Anhui Province (1508085QF134), the Innovation Foundation of Anhui Normal University (2016XJJ074).

摘要/Abstract

摘要： 针对现有的co-location模式挖掘算法无法有效处理不均匀分布空间对象的问题，提出一种不均匀模糊空间对象的分层次co-location模式挖掘方法。首先提出一种不均匀数据集的生成方法；然后对不均匀分布的数据集进行层次划分，使每个区域具有均匀的空间分布；再基于改进的PO_RI_PC算法对划分后的模糊对象进行空间数据挖掘。该方法基于距离变化系数构建每个子区域的邻域关系图，进而完成区域融合，实现co-location模式挖掘。实验结果表明，与传统方法相比，所提方法的执行效率更高，随实例个数和不均匀度的变化获得的co-location集个数更多，同比情况下平均提高约25%，获得了更精确的挖掘结果。

关键词: 模糊对象, co-location模式挖掘, 隶属度, 不均匀度, 距离变化系数

Abstract: Focusing on the issue that the existing co-location pattern mining algorithms fail to effectively address the problem of unevenly distributed spatial objects, a hierarchical co-location pattern mining approach of unevenly distributed fuzzy spatial objects was proposed. Firstly, an unevenly distributed dataset generation method was put forward. Secondly, the unevenly distributed dataset was partitioned by a hierarchical mining method in order to provide each region with an even spatial distribution. Finally, the spatial data mining of the separated fuzzy objects was conducted by means of the improved PO_RI_PC algorithm. Based on the distance variation coefficient, the neighborhood relationship graph for each sub-region was constructed to complete the regional fusion, and then the co-location pattern mining was realized. The experimental results show that, compared to the traditional method, the proposed method has higher execution efficiency. With the change of the number of instances and uneven degree, more co-location sets are mined, and the average increase reaches about 25% under the same condition, more accurate mining results are obtained through this method.

Key words: fuzzy object, co-location pattern mining, membership degree, uneven degree, distance variation coefficient

中图分类号:

TP311

俞庆英, 罗永龙, 吴倩, 陈传明. 不均匀模糊空间对象的分层次co-location模式挖掘方法[J]. 计算机应用, 2016, 36(11): 3113-3117.

YU Qingying, LUO Yonglong, WU Qian, CHEN Chuanming. Hierarchical co-location pattern mining approach of unevenly distributed fuzzy spatial objects[J]. Journal of Computer Applications, 2016, 36(11): 3113-3117.

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不均匀模糊空间对象的分层次co-location模式挖掘方法

Hierarchical co-location pattern mining approach of unevenly distributed fuzzy spatial objects

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