An Effective Algorithm for Mining High
Average-Utility Co-Location Patterns

Abstract

Abstract:

Co-location pattern is a subset of spatial feature set，and some instances of different features in co-location pattern frequently appear in adjacent areas. Looking at relevant research reports on high utility co-location pattern mining， the existing high utility co-location patterns mining algorithms do not consider the impact of the length of pattern on the utility of pattern. To explore this issue， we propose an effective algorithm of mining high average-utility co-location patterns （HAUCP） from spatial dataset to provide a better evaluation of the real utility of co-location pattern. First， a comprehensive definition of high average-utility co-location patterns based on spatial dataset is proposed. Secondly， the basic algorithm of high average-utility co-location pattern mining is constructed. For improving the efficiency of the basic algorithm， two pruning strategies are developed to reduce the computing costs. Finally， extensive experiments on real dataset and synthetic dataset are carried out to prove the effectiveness of the proposed algorithm. Experimental results show that high-utility co-location patterns of HAUCP are more reasonable.

Key words:

high average-utility, co-location patterns, pattern length, spatial dataset, data mining algorithm

CLC Number:

TP391

Zeng Xin, Zhang Xiaoling, Li Xiaowei.

An Effective Algorithm for Mining High Average-Utility Co-Location Patterns [J]. Journal of Dali University, 2022, 7(6): 9-17.

References

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An Effective Algorithm for Mining High Average-Utility Co-Location Patterns

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