On the Optimal Partitioning of Data with K-Means, Growing K-Means, Neural Gas, and Growing Neural Gas
Michał Daszykowski, Beata Walczak, Desire Luc Massart · Journal of Chemical Information and Computer Sciences · 2002
In this paper, the performance of new clustering methods such as Neural Gas (NG) and Growing Neural Gas (GNG) is compared with the K-means method for real and simulated data sets. Moreover, a new algorithm called growing K-means, GK, is introduced as the alternative to Neural Gas and Growing Neural Gas. It has small input requirements and is conceptually very simple. The GK leads to nearly optimal values of the cost function, and, contrary to K-means, it is independent of the initial data set partition. The incremental property of GK additionally helps to estimate the number of "natural" clusters in data, i.e., the well-separated groups of objects in the data space.