  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.
