Fuzzy subtractive clustering based prediction model for brand association analysis

Abstract

The brand is one of the crucial elements that determine the success of a product. Consumers in determining the choice of a product will always consider product attributes (such as features, shape, and color), however consumers are also considering the brand. Brand will guide someone to associate a product with specific attributes and qualities. This study was designed to identify the product attributes and predict brand performance with those attributes. A survey was run to obtain the attributes affecting the brand. Subtractive Fuzzy Clustering was used to classify and predict product brand association based aspects of the product under investigation. The result indicates that the five attributes namely shape, ease, image, quality and price can be used to classify and predict the brand. Training step gives best FSC model with radii (ra) = 0.1. It develops 70 clusters/rules with MSE (Training) is 9.7093e-016. By using 14 data testing, the model can predict brand very well (close to the target) with MSE is 0.6005 and its’ accuracy rate is 71%.

Comparison of Fuzzy Subtractive Clustering and Fuzzyc-Means

“…Another fuzzy clustering method, FSC also already applied on economic sector by Widodo [14] to form a prediction model of certain brand of products and by Chandar [15] to predict the stock market by applying subtractive clustering with neural fuzzy hybrid approach. FSC also once combined with wavelet to improve the low-cost navigation system with high speed [16].…”