The partition-based methods use a similarity measure to combine objects into groups. A practitioner usually selects the similarity measure for such kinds of algorithms by themself, using prior knowledge about a problem or heuristics to select the measure properly. Sometimes, several measures need to be tried with the same algorithm to choose the best one. Also, partition-based methods usually require either the number of desired clusters or a threshold that regulates the number of output clusters to be explicitly specified.