Comparing the Clustering Methods for User Centered Development

Interaction is very important for any information system. The quality of the product can be predicted by the user feedback. To improve the quality of service on the developer side, the developer needs to understand the user goals by analyzing the survey. The user goals, needs are to be captured with design tools. Qualitative and Quantitative clustering technique plays a major role in grouping the users based on their needs. The existing qualitative method becomes difficult as the number of users gets increased. Therefore the quantitative clustering techniques are also used to evaluate the goals. A method based on quantitative technique called Principal Component Analysis (PCA) is a data reduction technique, which is based on correlation. An automatic nonparameter Uncorrelated Discriminant Analysis (UDA) algorithm, satisfies the statistical correlation property. The outcome of the framework yields users goals to the developer community. Based on that, the developer would develop a product which is consensus with the user needs.