大理大学学报 ›› 2025, Vol. 10 ›› Issue (6): 11-17.

• 数学与计算机科学 • 上一篇    下一篇

带主导特征空间高效用亚频繁同位模式挖掘

  

  1. 大理大学数学与计算机学院,云南大理 671003
  • 出版日期:2025-06-15 发布日期:2025-06-24
  • 作者简介:赵秦怡,副教授,主要从事空间数据挖掘算法研究。
  • 基金资助:
    云南省地方本科高校基础研究联合专项资金项目(202301BA070001-034)

Mining of Spatial High Utility Sub-Prevalent Co-Location Patterns with Dominant Features

  1. College of Mathematics and Computer Science, Dali University, Dali, Yunnan 671003, China
  • Online:2025-06-15 Published:2025-06-24

摘要: 亚频繁同位模式是同位模式的一种,其挖掘基于星型实例模型。针对实例集中不带特征效用值和实例效用值的情况,研究如何基于星型实例模型计算星型参与实例的效用值,并提出一种特征星型效用参与率的计算方法。在同位模式主导特征挖掘中,现有研究很少考虑特征实例之间的距离分布对主导关系的影响。因此,研究如何将星型参与实例与邻居实例间的距离分布作为特征间的主导权重,并提出一种加权的特征星型主导率计算方法。最后,提出一种带主导特征的空间高效用亚频繁同位模式挖掘算法。实验结果表明,该算法能够有效区分模式的效用度及其主导特征,且算法的有效性较为显著。

关键词: 同位模式, 亚频繁同位模式, 特征星型效用参与率, 加权特征主导率, 星型参与实例效用值

Abstract: Sub-prevalent co-location pattern is a subset of co-location patterns, and its mining is based on the star instances model.
In the absence of feature utility values and instance utility values in instance sets, this study investigates how to calculate the utility
value of star participating instances based on the star instances model, and proposes a method for calculating the feature star utility
participation rate. In co-location pattern dominant feature mining, existing research rarely considers the influence of distance
distribution between feature instances on dominant relationships. Therefore, this study explores how to use the distance distribution
between star participating instances and neighbor instances as the dominant weight between features, and proposes a weighted method
for calculating the feature star dominance rate. Finally, an algorithm for mining spatial high utility sub-prevalent co-location patterns
with dominant features is proposed. The experimental results show that this algorithm can effectively distinguish the utility values and
dominant features of patterns, and its effectiveness is significant.

Key words: co-location patterns, sub-prevalent co-location pattern, feature star utility participation rate, weighted feature dominance
rate,
utility value of star participating instances

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