Data mining techniques can be used to discover useful patterns by exploring
and analyzing data and it's feasible to synergitically combine machine learning
tools to discover fuzzy classification rules.In this paper, an adaptive Neuro
fuzzy network with TSK fuzzy type and an improved quantum subtractive
clustering has been developed. Quantum clustering (QC) is an intuition from
quantum mechanics which uses Schrodinger potential and time-consuming gradient
descent method. The principle advantage and shortcoming of QC is analyzed and
based on its shortcomings, an improved algorithm through a subtractive
clustering method is proposed. Cluster centers represent a general model with
essential characteristics of data which can be use as premise part of fuzzy
rules.The experimental results revealed that proposed Anfis based on quantum
subtractive clustering yielded good approximation and generalization
capabilities and impressive decrease in the number of fuzzy rules and network
output accuracy in comparison with traditional methods.

本文提出了一种自适应的神经模糊网络和一种改进的量子减法聚类，将数据挖掘技术与机器学习结合，发现模糊分类规则。实验结果表明，基于量子减法聚类的神经模糊网络具有较好的逼近和概括能力，并且与传统方法相比，模糊规则的数量和网络输出精度有显著降低。