Similarity-Driven Cluster Merging Method for Unsupervised Fuzzy Clustering
This is an incremental improvement for researchers in unsupervised clustering, addressing validation issues in fuzzy clustering methods.
The paper tackles the problem of cluster validation in unsupervised fuzzy clustering by proposing a similarity-driven cluster merging method that starts with an overspecified number of clusters and merges similar pairs based on a fuzzy cluster similarity matrix and adaptive threshold, with experiments illustrating its properties.
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.