Video data captured and uploaded under volatile conditions is accumulating into a flood of unstructured content that is hard to manage. In this paper, we present a set of algorithms that generate numeric or soft labels to structure automatically and produce video similarity rankings. Exemplar frames are extracted from each video during the labeling process. We propose five types of learning algorithms based on the following three classes: affinity propagation, k-means or harmonic competition, and pairwise nearest-neighbor. For redundancy reduction, we also provide an algorithm that prunes excessive exemplars. The five learning algorithms produced creditable order-aware exemplar sets for the target videos. Because the content and lengths of the videos differ in terms of temporal order, we provide new methods to analyze the similarities between exemplar sets. The m-distance similarity measure is the core concept used for the global and local alignments performed on the obtained exemplar sets. Based on this comparison mechanism, we identified high-precision recall curves for all five methods. In terms of learning speed, the k-means and pairwise nearest-neighbor classes are recommendable. To facilitate similar-video retrieval, we developed a graphical user interface that accepts videos downloaded from the Web. By replacing a procedure in the software, the proposed similar-video retrieval system can accommodate more elaborate frame comparison features.