[1]金佩洁,王晓璇,熊文,等.基于模糊特征簇的空间高效用并置模式挖掘算法[J].计算机技术与发展,2024,34(12):132-140.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0250]
　JIN Pei-jie,WANG Xiao-xuan,XIONG Wen,et al.Spatial High Utility Co-location Pattern Mining Based on Fuzzy Feature Clusters[J].,2024,34(12):132-140.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0250]
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基于模糊特征簇的空间高效用并置模式挖掘算法()

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
34
期数:
2024年12期
页码:
132-140
栏目:
人工智能
出版日期:
2024-12-10

文章信息/Info

Title:
Spatial High Utility Co-location Pattern Mining Based on Fuzzy Feature Clusters
文章编号:
1673-629X(2024)12-0132-09
作者:
金佩洁1; 2; 王晓璇1; 2; 熊文1; 2; 王丽珍3; 高嵩4
1. 云南师范大学 信息学院,云南 昆明 650500;2. 云南省教育厅 计算机视觉与智能控制技术工程研究中心,云南 昆明 650500;3. 云南大学 滇池学院,云南 昆明 650500;4. 云南大学 软件学院,云南 昆明 650500
Author(s):
JIN Pei-jie1; 2; WANG Xiao-xuan1; 2; XIONG Wen1; 2; WANG Li-zhen3; GAO Song4
1. School of Information Science and Technology,Yunnan Normal University,Kunming 650500,China;2. Engineering Research Center of Computer Vision and Intelligent Control Technology,Department of Education of Yunnan Province,Kunming 650500,China;3. Dianchi College,Yunnan University,Kunming 650500,China;4. National Pilot School of Software,Yunnan University,Kunming 650500,China
关键词:
空间数据挖掘; 空间并置模式; 高效用; 模糊聚类; 模糊特征簇
Keywords:
spatial data mining; spatial co-location pattern; high utility; fuzzy clustering; fuzzy feature cluster
分类号:
TP306.1
DOI:
10.20165/j.cnki.ISSN1673-629X.2024.0250
摘要:
实例经常出现在相邻区域的空间特征集被称为空间并置模式。 空间高效用并置模式是空间并置模式挖掘的一项扩展性研究,其考虑了模式的效用价值,更能反映出空间特征间的高质量聚集现象。 现有的空间高效用并置模式挖掘方法大多使用自适应的效用参与度(Utility Participation Index,UPI)作为度量参数。 然而,UPI 没有考虑真实地理环境中“邻近”概念的模糊性和重叠性。 此外,由于 UPI 不满足向下闭合性,算法的挖掘效率也不够理想。 因此,该文结合模糊集理论构建了空间模糊邻近关系,提出了新的模糊效用度计算方法。 同时,引入了模糊 Chameleon 聚类算法,提取了基于模糊特征效用度的模糊特征簇,从而进一步提取空间高效用并置模式。 最后,在三个模拟数据集和两个真实数据集上进行了大量的实验,证明了该算法的合理性及有效性。
Abstract:
Sets of spatial features whose instances frequently appear together in nearby areas are regarded as spatial co-location patterns.Spatial high utility co-location patterns is an extended research of spatial co-location pattern mining,which better reflects the high - quality aggregation phenomenon among spatial features. Most of the existing methods use the adaptive utility participation index (UPI) as a metric parameter for mining high utility co - locations. However,UPI does not take into account the influence of fuzziness and overlap of proximity relationships on the utility of co - location patterns. Furthermore, UPI does not satisfy the downward closure property,the mining efficiency is still not satisfactory. Combining fuzzy set theory,we establish fuzzy neighbor relationships and propose a new method for calculating the utility of co-location patterns. At the same time,we generate the fuzzy high utility feature clusters by the Fuzzy Chameleon Clustering algorithm,and extract spatial high utility co-location patterns from these high utility clusters. Finally,a large number of experiments are conducted on three synthetic datasets and two real datasets,which prove the rationality and effectiveness of the proposed algorithms.

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更新日期/Last Update: 2024-12-10