Parameter extension of q-divergence-based fuzzy c-means clustering
提出了一种扩展的基于q散度的模糊c均值聚类算法(eQFCM)，通过将模糊化参数从两个扩展到四个，提高了聚类精度，并在人工和真实数据集上验证了其优于原有算法。
Abstract In this study, we propose a fuzzy clustering algorithm for vectorial data, which is constructed by extending the fuzzification parameters in the q-divergence-based fuzzy c-means algorithm (QFCM). The proposed algorithm, referred to as extended QFCM (eQFCM), is an extension of both QFCM and the penalized fuzzy c-means algorithm proposed by Yang, referred to as Y-type FCM (YFCM). eQFCM extends both the two-parameter QFCM and YFCM algorithms to a four-parameter model. Through numerical experiments using an artificial dataset, we substantiate the theoretical discussion, and the effects of fuzzification parameter to clustering results are observed. Furthermore, the results of some numerical experiments using real datasets are presented to demonstrate that the proposed algorithm outperformed both QFCM and YFCM algorithms in terms of clustering accuracy.