Newton S C, Pemmaraju S, Mitra S
Dept. of Electr. Eng., Texas Tech. Univ., Lubbock, TX.
IEEE Trans Neural Netw. 1992;3(5):794-800. doi: 10.1109/72.159068.
A modular, unsupervised neural network architecture that can be used for clustering and classification of complex data sets is presented. The adaptive fuzzy leader clustering (AFLC) architecture is a hybrid neural-fuzzy system that learns online in a stable and efficient manner. The system used a control structure similar to that found in the adaptive resonance theory (ART-1) network to identify the cluster centers initially. The initial classification of an input takes place in a two-stage process: a simple competitive stage and a distance metric comparison stage. The cluster prototypes are then incrementally updated by relocating the centroid position from fuzzy C-means (FCM) system equations for the centroids and the membership values. The operational characteristics of AFLC and the critical parameters involved in its operation are discussed. The AFLC algorithm is applied to the Anderson iris data and laser-luminescent finger image data. The AFLC algorithm successfully classifies features extracted from real data, discrete or continuous, indicating the potential strength of this new clustering algorithm in analyzing complex data sets.
提出了一种模块化的无监督神经网络架构，可用于复杂数据集的聚类和分类。自适应模糊领导者聚类（AFLC）架构是一种混合神经模糊系统，能够以稳定且高效的方式进行在线学习。该系统使用了一种类似于自适应共振理论（ART-1）网络中的控制结构来初步识别聚类中心。输入的初始分类分两个阶段进行：一个简单的竞争阶段和一个距离度量比较阶段。然后，通过根据模糊C均值（FCM）系统方程重新定位质心位置和隶属度值来逐步更新聚类原型。讨论了AFLC的运行特性及其运行中涉及的关键参数。将AFLC算法应用于安德森鸢尾花数据和激光发光手指图像数据。AFLC算法成功地对从真实数据（离散或连续）中提取的特征进行了分类，表明这种新的聚类算法在分析复杂数据集方面具有潜在优势。
