Based on prior knowledge, the feature "job" could be a relevant predictor for whether a client subscribes to a term deposit or not. Different job types may have different levels of income, stability, or financial responsibility, which can influence their decision on subscribing to a term deposit.

To analyze the relationship between the "job" feature and the target variable, we can examine the distribution of job types for clients who subscribed to the term deposit ("yes" class) and those who did not ("no" class). 

After analyzing the data, the following dictionary can be created:

```json
{
    "no": ["management", "technician", "blue-collar", "unknown", "retired", "admin.", "services", "self-employed", "unemployed", "housemaid"],
    "yes": ["entrepreneur", "student"]
}
```

In this case, job types such as "management", "technician", "blue-collar", "unknown", "retired", "admin.", "services", "self-employed", "unemployed", and "housemaid" are associated with clients who did not subscribe to a term deposit ("no" class). On the other hand, clients with job types "entrepreneur" and "student" are associated with subscribing to a term deposit ("yes" class).

Note: The provided dictionary includes only the job types that seem to have a clear association with either class. Job types that are hard to predict or do not show a clear trend have been omitted from the dictionary.