## 2 Literature Review
### ARM Methodology  
The ARM is a process of deriving a statistically significant relationship with a specific event, article, or item in the database [27]. ARM has traditionally been developed to analyze distributors' POS data based on sales and product information and is most widely used in the marketing field. When a customer purchases an item at the checkout counter of a store, customer information and purchase item information are input together, which is called market basket data, and the purchase item information included in one customer, that is, one transaction. There are many forms of transaction in basket data, and the Association Rule Mining technique is to find the association and pattern of transaction through this large amount of data. This associated rule analysis model is used by product sales companies to establish marketing strategies by predicting which products customers who purchase specific products will purchase together. It can also be used to change the store display method in connection with customer movements, or to develop package products. From this point of view, this study intends to apply ARM, a data mining technique, to identify the relationship between design change items that occur during construction work and the mechanism of their occurrence. By applying the ARM model to this study, comparing \"market basket data\" and \"design change data\" of apartment housing project, \"one customer\" can be applied as \"a project\" and \"purchase product information\" as a \"design change item\". Through this, it is possible to find the association and pattern between design change items that occurred in multiple projects. In particular, it is possible to increase a certain pattern and reliability of the occurrence of design changes by analyzing a large amount of design change data of a new apartment building project having a similar shape, scale, plan, and construction method. Using the ARM approach, it is possible to discover meaningful rules among items by the deriving 'condition-result (if-then)' patterns between items [28]. This if-then can be defined as the relationship between the conditional part of the data group and the conclusion part rather than the meaning of the \"if-then\" in logic. Essentially, the purpose is to identify events with a high probability of occurrence by finding hidden patterns in the data group. In association rules, scales such as support, confidence, and lift are used to select meaningful rules from among the patterns. * Support: Support for two items x and y indicates the ratio of the number of data including both item sets x and y among the total number of data. Support is used as a basis for determining good rules (high frequency or high composition ratio) or reducing unnecessary operations (Pruning). * Y) = P(X
* Confidence: The proportion of data containing item Y among data with item X (conditional probability). A more reliable rule is more likely to be useful. * Y) = P(X
- Y) = P(X
* Lift: There is a limit to discovering a meaningful association rule only with support and confidence, and it is measured to discover new rules. Lift is expressed as the ratio of the rule confidence to the rule expected confidence. In other words, the lift index generally indicates the dependence between the items in the item set and can be an index indicating the strength of the association rule. If the lift index is greater than (positive relationship) or less (negative relationship) than 1, it indicates that it is better than a random chance. If they are independent, lift = 1, and the expected confidence of a rule is defined as the value obtained by dividing the value obtained by multiplying the support of the left and right sides by the support of the left side. \[\text{Lift}(X\rightarrow Y)=P(Y\,|\,X)/P(Y)=P(X\cap Y)/P(X)P(Y)\]  
The three aforementioned scales are important criteria for adopting derived association, such as minimum support, minimum confidence, and minimum lift. Determining the threshold has a decisive influence on rule adoption.