A Combined Co-location Pattern Mining Approach for Post-Analyzing Co-location Patterns
作者： Yuan Fang , Lizhen Wang , Junli Lu , Lihua Zhou
DOI: 10.2991/ICAITA-16.2016.10
关键词: Measure (mathematics) 、 Limit (mathematics) 、 Usability 、 Location pattern 、 Data set 、 Data mining 、 Series (mathematics) 、 Computer science
摘要: * Corresponding author Abstract—The co-location pattern mining discovers the subsets of spatial features which are located together frequently in geography. However, huge number results limit usability patterns. Furthermore, users hardly identify and understand interesting knowledge directly from single pattern.In this paper, we studied problem extractingcombined patterns a large collectionof prevalent patterns.We first gave definitions atomic pattern, combined pair cluster; secondly, designed series metrics to measure interestingness patterns, pairs clusters; thirdly, an algorithm redundant elimination strategies were proposed. The experiments evaluated method both on real data sets syntheticdata sets. show that our can effectively discover Keywords-co-location mining; post-analysis
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