## 4 Results and Discussion
### Apriori Analysis Results  
To run the association analysis, the antecedent data of the eight independent conditioning factors, each classified into five index values (Figure 7), and the consequent slope formation inventory data in four classes were stacked into one data frame. New counts of the inventory classes (after generating random values in the polygon around the original points) became landslide (2869), vulnerable slopes (151), unlabeled (730), and no-landslide (209). The selected conditioning factors had been previously shown to be effective in susceptibility analysis with landslide class alone [28]. Figure 6: t-SNE run results using different step numbers and perplexity limits for inventory classes: no landslide (red dots), landslide (black dots), vulnerable slopes (blue dots), and unlabeled (green dots). t-SNE, t-distributed stochastic neighbor embedding. Settings for the initial Apriori function parameters (support = 50%, confidence = 70%, max = 6%, and min = 2%) were applied to each class of the inventory separately. Generally, the conditioning factors included all five classes, but varied in amount. However, the distance to drains factors included just \"low\" and \"very low\" classes. Now, rather than mention each conditioning factor class and its corresponding frequency of inventory classes, the validity of using the association rules, offer to find the areas that share significant importance in landslide and unlabeled features occurrence with reference to other factor classes (Figure 8). Figure 7: Fisher’s Junks breaks in continuous conditioning factor values, green to red (very low, low, moderate, high, and very high). Since there were many points in the landslide class compared to other classes, and to avoid the consequent risk of overfitting, we ran the function for each class separately. Apriori \"association rules\" were constructed in such a way that input could be adjusted according to its functionality. Conditioning factors, the \"antecedent,\" were placed on the left-hand side of the Apriori function, while the landslide inventory, the \"consequent,\" was placed on the right-hand side. Figure 8 shows the relationship in terms of association rules for each inventory class. These were optimized by applying specific constraints for clear interpretation. The results, produced by the machine learning function, showed 50-76% support and 100% confidence using six, six, seven, and eight rules to describe the associations for the inventory classes, landslide, no-landslide, vulnerable slopes, and unlabeled, respectively. The vulnerable slopes class represents slopes condition that did not cause slope failure over a long period, specifically from the main typhoons of 1998-2017. Consequently, according to field inspection, these areas were mostly on moderate slopes with thick vegetation cover and surrounded by different land uses such as infrastructure assets. In this study, we sought to identify the original structure of the unlabeled class in the inventory; therefore, we identified the common antecedents that are particularly associated with that class. Very low distance to drains (0-105 m) was found in all inventory classes, but was most common in landslide and unlabeled classes, reflecting observed landslide occurrence in most studies [29]. In a similar study, [PERSON] et al. [28] produced landslide  
Figure 8: Apriori analysis results showing the rules relating the antecedents to the consequents (in the center), lift value = 1 for all classes: (**a**) no-landslide (six rules and support 0.544–0.723). (**b**) Vulnerable (seven rules and support 0.522–0.713). (**c**) Landslide (six rules and support 0.521–0.744), and (**d**) unlabeled (eight rules and support 0.55–0.73). susceptibility maps that support this finding.