Abstract: In addressing the substantial data volume within landslide monitoring databases and the lengthy processing times due to multiple database scans required for association rule analysis, we introduce the Eclat association rule algorithm into landslide monitoring data mining. This approach involves analyzing the deformation of the Bazimen landslide using the K-means clustering method and the Eclat algorithm. Through comprehensive investigation, we identify six factors from rainfall monitoring values and reservoir water level monitoring values for data mining and analysis. By uncovering the correlations of three rainfall factors and three reservoir water level factors with the displacement of multiple measurement points in the Bazimen landslide, we extract eight association rules with a high confidence level from all excavated correlation rules derived from the spatiotemporal monitoring big data of the Bazimen landslide. This analysis reveals effective information of rainfall and water level influencing landslide movement. The findings indicate the potential widespread applicability of this data mining method due to its high accuracy in monitoring data research, particularly in the analysis and prediction of accumulation landslides within reservoir areas.

Key words: Bazimen landslide, Eclat algorithm, association rules, data mining, Three Gorges Reservoir area