Application of Incremental Updating Association Mining Algorithm in Geological Disasters System

Aiming at the problems of low efficiency, low cost of time and space, This paper proposes an algorithm to update the association mining of the inverted index tree. The algorithm combines the inverted index technology with the tree structure. When the data in the database is continuously updated, it can scan only the newly added part of the database, without having to scan the original database to count the number of transaction items. The optimal threshold predicted by Newton’s interpolation formula is compared with this frequency to get frequent item sets. Then, the confidence level is calculated for the combinations of different item sets in frequent item sets, and the correlation rules are obtained, and the correlation analysis of the rules is carried out to obtain a more realistic association rule. The inverted index tree updating association mining algorithm was applied to the data analysis of geological hazards monitoring system. One year data record of rainfall, groundwater level, soil water content and topography data was selected as the experimental data set. Compared with the IUAR algorithm, it is found that the inverted index tree updating association mining algorithm has some improvements in memory consumption and efficiency. The experimental results show that When the minimum support of IUAR algorithm remains unchanged, the number of transaction records is the same as the amount of new data, and Inverted Index Tree Incremental Updating Association Mining Algorithm takes less than 2/5 of the IUAR algorithm. When the number of transaction records and the amount of new data remain unchanged and IUAR algorithm support changes, the Inverted Index Tree Incremental Updating Association Mining Algorithm memory consumption is much smaller than IUAR algorithm. In the process of experiment, according to the results of the Inverted Index Tree Incremental Updating Association Mining Algorithm, the association rules are obtained and the correlation is judged. The strong association rules are used to set the alarm threshold of the geological disaster monitoring system.