Different sequential clustering algorithms and sequential regression models
Sadaaki Miyamoto, Kenta Arai · 2009
Three approaches to extract clusters sequentially so that the specification of the number of clusters beforehand is unnecessary are introduced and four algorithms are developed. First is derived from possibilistic clustering while the second is a variation of the mountain clustering using medoids as cluster representatives. Moreover an algorithm based on the idea of noise clustering is developed. The last idea is applied to sequential extraction of regression models and we have the fourth algorithm. We compare these algorithms using numerical examples.