摘要
co-location模式是空间特征集的一个子集,其特征实例在地理空间中频繁出现互相近邻,基于特征参与率进行模式挖掘,特征参与率定义为模式表实例中不重复的实例个数与特征总实例数的比率。针对基于指定特征的模式特征实例并置程度满足模式指导性要求,但部分特征总实例数过多而导致特征参与率小于阈值,模式被界定为非频繁模式的情况,提出一种基于指定特征的加权co-location模式挖掘方法。定义特征的权以及特征加权参与率计算规则,可以有效挖掘基于指定特征的加权co-location模式,其加权参与度随着模式阶数的增大而单调递减。实验结果证明了该算法在挖掘结果及算法运行时间上的有效性。
A co-location pattern is a subset of spatial feature set whose feature instances frequently appear in proximity in geographic space.Pattern mining is carried out based on the feature participation rate,which is defined as the radio of the number of unique instances in the pattern table to the total number of feature instances.A weighted co-location pattern mining method based on specified features is proposed to address the situation where the degree of concatenation of pattern feature instances based on specified features satisfies the pattern guidance requirements,but some features have too many total instances leading to a feature participation rate below the threshold,and the pattern is defined as a non-frequent pattern.The proposed method defines the weight of features and the calculation rules of weighted participation rate,which can effectively mine weighted co-location patterns based on specified features,and the weighted participation degree decreases monotonically with the increase of the pattern order.The experimental results indicate the effectiveness of the algorithm in terms of mining results and algorithm runtime.
作者
赵秦怡
黑邵敏
Zhao Qinyi;Hei Shaomin(College of Mathematics and Computer,Dali University,Dali,Yunnan 671003,China)
出处
《大理大学学报》
2023年第12期15-21,共7页
Journal of Dali University
