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These two key differences point toward some situations in which the semi-supervised approach is preferable. 1. In some clustering problems the desired similarity metric may be so different from the default that traditional active learning would make many inefficient queries. This problem also arises when there are many different plausible clusterings. Although less automated, a human browsing the data would do less work by selecting the feedback data points themself. 2. The intuitive array of possible constraints are easier to apply than labels, especially when the final clusters are not known in advance.
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