Spatial co-location pattern mining based on the improved density peak clustering and the fuzzy neighbor relationship

Area of Science:

• Geographic Information Science
• Data Mining
• Computational Geometry

Background:

• Spatial co-location pattern mining identifies frequently co-occurring spatial features.
• Existing methods using density peak clustering face challenges with distance fuzziness and inaccurate fuzzy participation index calculations.

Purpose of the Study:

• To address the limitations of traditional density peak clustering in spatial co-location pattern mining.
• To propose an improved algorithm for more accurate and efficient discovery of spatial co-location patterns.

Main Methods:

• Developed three improvement strategies for density peak clustering.
• Introduced a novel prevalence measurement for co-location patterns.
• Designed a spatial co-location pattern mining algorithm integrating improved density peak clustering and fuzzy neighbor relationships.

Main Results:

• The proposed algorithm demonstrates superior effectiveness compared to existing methods.
• Significant savings in time and space complexity were achieved during the generation of prevalent co-location patterns.
• Experimental results on synthetic and real datasets validate the algorithm's performance.

Conclusions:

• The improved density peak clustering approach enhances spatial co-location pattern mining.
• The new algorithm offers a more accurate and computationally efficient solution for discovering spatial patterns.