Answer: (iii) legitimate and illegitimate data objects

In data mining (per Han, Kamber & Pei), outliers are data objects that deviate considerably from the general behaviour or distribution of the rest of the data — and they can be both legitimate and illegitimate:

• Legitimate outliers — genuine data objects that differ from the majority but are still valid data; they themselves are the main interest in some mining tasks (e.g., fraud detection — an unusual transaction is a valuable event worth investigating, not a mistake).
• Illegitimate outliers — objects that arise from noise or errors (sensor malfunction, human data-entry mistakes, measurement glitches); these are unwanted and are usually cleaned/removed during preprocessing.

Because outlier analysis deals with both cases, option (iii) — "legitimate and illegitimate data objects" — is the correct characterization.

Why the others are wrong:

• (i) legitimate only and (ii) illegitimate only — each captures only one side; outliers can be either.
• (iv) None of the above — incorrect since (iii) is correct.