## 4 Results
### Association Rule Mining Results  
After clustering, each cluster is named, and then the values in the features converted into category names. The category names of different features at each moment form an item set, thereby transforming the entire feature dataset into a transaction database. The Apriori algorithm was used to carry out the association rule mining study on this transaction database to mine strong association rules between rainfall features and the velocities of two GNSS monitoring stations separately. We took the velocity of GNSS monitoring stations as the latter term and the rainfall characteristics as the former term, and obtained the corresponding strong association rules based on both different \(min\_conf\) and \(min\_supp\). For the velocity of the DB01 monitoring station, we set the \(min\_supp\) as 0.3% and the \(min\_conf\) as 80%. For landslide warning, we focused more on the high-speed deformation process, which is the DB01-High-Velocity cluster, so we filtered the eligible association rules as shown in Table 3. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Feature Name** & **Cluster Name** & **Lower Bound** & **Upper Bound** & **Count** & **Mean** &
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} \\ \hline \multirow{3}{*}{\(v_{DB01}\)} & DB01-Low-Velocity & \(-3.64\) & \(4.70\) & \(4887\) & \(0.46\) & \(1.02\) \\  & DB01-Medium-Velocity & \(4.78\) & \(15.54\) & \(253\) & \(9.09\) & \(2.83\) \\  & DB01-High-Velocity & \(16.49\) & \(60.96\) & \(56\) & \(23.58\) & \(6.25\) \\ \hline \multirow{3}{*}{\(v_{DB02}\)} & DB02-Low-Velocity & \(-3.84\) & \(4.77\) & \(4669\) & \(0.60\) & \(1.23\) \\  & DB02-Medium-Velocity & \(4.79\) & \(55.37\) & \(507\) & \(13.39\) & \(8.55\) \\  & DB02-High-Velocity & \(59.24\) & \(108.84\) & \(20\) & \(90.67\) & \(15.85\) \\ \hline \multirow{3}{*}{\(q^{34}\)} & Rain-3 h-Low & \(0.00\) & \(3.60\) & \(4962\) & \(0.18\) & \(0.56\) \\  & Rain-3 h-Medium & \(3.80\) & \(19.40\) & \(217\) & \(7.33\) & \(3.43\) \\  & Rain-3 h-High & \(21.20\) & \(61.40\) & \(17\) & \(34.34\) & \(12.17\) \\ \hline \multirow{3}{*}{\(q^{64}\)} & Rain-6 h-Low & \(0.00\) & \(5.00\) & \(4830\) & \(0.32\) & \(0.88\) \\  & Rain-6 h-Medium & \(5.20\) & \(24.40\) & \(333\) & \(9.95\) & \(4.57\) \\  & Rain-6 h-High & \(24.80\) & \(83.80\) & \(33\) & \(39.42\) & \(16.47\) \\ \hline \multirow{3}{*}{\(q^{12k}\)} & Rain-12 h-Low & \(0.00\) & \(7.20\) & \(4656\) & \(0.64\) & \(1.48\) \\  & Rain-12 h-Medium & \(7.40\) & \(32.80\) & \(494\) & \(13.93\) & \(6.12\) \\  & Rain-12 h-High & \(33.40\) & \(89.20\) & \(46\) & \(52.80\) & \(17.83\) \\ \hline \multirow{3}{*}{\(q^{24k}\)} & Rain-24 h-Low & \(0.00\) & \(10.40\) & \(4429\) & \(1.39\) & \(2.55\) \\  & Rain-24 h-Medium & \(10.60\) & \(43.00\) & \(698\) & \(19.64\) & \(7.76\) \\  & Rain-24 h-High & \(45.00\) & \(99.40\) & \(69\) & \(68.48\) & \(17.00\) \\ \hline \multirow{3}{*}{\(q^{34}\)} & Rain-3 d-Low & \(0.00\) & \(17.20\) & \(3736\) & \(4.31\) & \(5.12\) \\  & Rain-3 d-Medium & \(17.40\) & \(68.20\) & \(1284\) & \(30.46\) & \(11.61\) \\  & Rain-3 d-High & \(68.60\) & \(202.20\) & \(176\) & \(106.48\) & \(28.99\) \\ \hline \multirow{3}{*}{\(q^{74}\)} & Rain-7 d-Low & \(0.00\) & \(35.20\) & \(3554\) & \(15.46\) & \(10.88\) \\  & Rain-7 d-Medium & \(35.40\) & \(122.20\) & \(1450\) & \(55.17\) & \(17.47\) \\ \cline{1-1}  & Rain-7 d-High & \(130.60\) & \(285.80\) & \(192\) & \(197.53\) & \(46.03\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Interval range and sample size of all feature clustering results. For the velocity of DB02 monitoring station, we set the \(min\_supp\) as 0.1% and the \(min\_conf\) as 80%. We also filtered the association rules with DB01-High-Velocity as the latter term in the same way (see Table 3).