MATEC Web Conf.
Volume 154, 2018The 2nd International Conference on Engineering and Technology for Sustainable Development (ICET4SD 2017)
|Number of page(s)||6|
|Section||Engineering and Technology|
|Published online||28 February 2018|
Fuzzy subtractive clustering based prediction model for brand association analysis
Industrial Engineering Department, Universitas Islam Indonesia, Yogyakarta, 55584, Indonesia
* Corresponding author: email@example.com
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%.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.