Conventional clustering techniques provide a static snapshot of each vector's commitment to every group. With additive datasets, however, existing methods may not be sufficie...
We present an online adaptive clustering algorithm in a decision tree framework which has an adaptive tree and a code formation layer. The code formation layer stores the represen...
Bayesian Information Criterion (BIC) is a promising method for detecting the number of clusters. It is often used in model-based clustering in which a decisive first local maximum ...
Abstract—In contrast to standard fuzzy clustering, which optimizes a set of prototypes, one for each cluster, this paper studies fuzzy clustering without prototypes. Starting fro...
We derive the clustering problem from first principles showing that the goal of achieving a probabilistic, or ”hard”, multi class clustering result is equivalent to the algeb...