unsupervised clustering

Most clustering algorithms are unsupervised, simply, taking groups of values and associating items based on similarity of attributes. Of cousre, the idea of what similarty means for a oaetickar data set does have to be specified by the algorithm user, or is a fixed feature of the algorithm. Examples include k-means and self-organising maps.