Based on prior knowledge, we can analyze the relationship between the "maint" feature and the decision to buy the car. 

Looking at the feature description, we can assume that the price of maintenance can have an impact on how someone would rate the decision to buy the car. Generally, if the maintenance cost is low, it is more likely that the decision to buy the car would be rated as good or very good. On the other hand, if the maintenance cost is high, it is more likely that the decision to buy the car would be rated as unacceptable or acceptable.

Based on this analysis, we can create a dictionary as follows:

```json
{
	"unacceptable": ["very high", "high"],
	"acceptable": ["very high", "high", "medium"],
	"very good": ["low"],
	"good": ["low", "medium"]
}
```

In this dictionary, we have categorized the "maint" feature values into different target classes. The values "very high" and "high" are associated with the target class "unacceptable" and "acceptable". The value "low" is associated with the target class "very good" and "good". The value "medium" is associated with the target class "acceptable" and "good".