Generalized Normalized Euclidean Distance Based Fuzzy Soft Set Similarity for Data Classification

Classification is one of the data mining processes used to predict predetermined target classes with learning accurately. This study discusses classification using a fuzzy soft set method aims form algorithm method. In this study, was calculated based on normalized Hamming distance. Each parameter in mapped power from subset approximation function. step, generalized Euclidean distance determine similarity between two sets sets. The experiments University California (UCI) Machine Learning dataset assess accuracy proposed samples were divided into training (75% samples) and test (25% Experiments performed MATLAB R2010a software. showed that: (1) fastest sequence matching function, measure, similarity, distance, (2) approach can improve recall by up 10.3436% 6.9723%, respectively, compared baseline techniques. Hence, appropriate for classifying data.