Based on prior knowledge, we can analyze the relationship between the feature "previous" and the target variable "Does this client subscribe to a term deposit?".

The "previous" feature represents the number of contacts performed before this campaign and for this client. In this case, it could indicate the client's familiarity with the campaign or the number of times they have been contacted before.

To analyze the relationship, we can consider the following:

1. Clients with a higher number of previous contacts may be more likely to subscribe to a term deposit as they have shown interest or engagement in the past.
2. Clients with no previous contacts or a low number of contacts may be less likely to subscribe as they have not shown much interest or engagement previously.
3. There might be a threshold value beyond which further contacts do not significantly impact the likelihood of subscription.

Based on this analysis, we can create a dictionary with typical previous values for each target class:

```json
{
	"no": [0.0, 1.0, 2.0, 3.0, 4.0],  
	"yes": [1.0, 2.0, 3.0, 4.0, 5.0]  
}
```

This dictionary suggests that typical values for the "previous" feature for clients who did not subscribe to a term deposit ('no' class) may range from 0.0 to 4.0. On the other hand, typical values for clients who did subscribe to a term deposit ('yes' class) may range from 1.0 to 5.0.

Please note that the actual ranges of values for the "previous" feature would depend on the specific dataset and should be determined through exploratory data analysis or domain knowledge.