A Method of Mining Spatial High Utility Co-location Patterns Based on Feature Actual Participation Weight

Abstract

Spatial high utility co-location patterns mining takes pattern utility participation index as interest measure, but it still lacks standard method for calculating pattern utility participation index. This paper presents some reasonable definitions of spatial high utility colocation patterns based on the datasets of instance with utility value, such as Feature Actual Participation Weight, Feature Utility Participation Index and Pattern Utility Participation Index and many more. A basic algorithm BWHMA(high utility co-location patterns mining based on feature actual participation weight) is proposed. For improving the efficiency of algorithm BWHMA, one pruning algorithm FUPRA(based on feature utility participation rate) and one optimization algorithm FRPWA(based on feature actual participation weight) are put forward. Finally, a large number of comparative experiments have been done on both synthetic and real datasets. The experiments show that the proposed algorithms are feasible, correct and valid.