In this paper, a similarity-driven cluster merging method is proposed for
unsuper-vised fuzzy clustering. The cluster merging method is used to resolve
the problem of cluster validation. Starting with an overspecified number of
clusters in the data, pairs of similar clusters are merged based on the
proposed similarity-driven cluster merging criterion. The similarity between
clusters is calculated by a fuzzy cluster similarity matrix, while an adaptive
threshold is used for merging. In addition, a modified generalized ob- jective
function is used for prototype-based fuzzy clustering. The function includes
the p-norm distance measure as well as principal components of the clusters.
The number of the principal components is determined automatically from the
data being clustered. The properties of this unsupervised fuzzy clustering
algorithm are illustrated by several experiments.

本文提出了一种相似性驱动的聚类合并算法，用于解决聚类验证问题，算法采用聚类相似度矩阵计算相似性，自适应阈值进行合并，同时使用具有 p - 范数距离和聚类主成分的修改广义目标函数来进行非监督聚类。