The application of rough set and Kohonen network to feature selection for object extraction

Selecting a set of features which is optimal for a given task is a problem which plays an important role in a wide variety of contexts including pattern recognition, images understanding and machine learning. The paper describes an application of rough sets method to feature selection and reduction in texture images recognition. The proposed methods include continuous data discretization based on Kohonen neural network and maximum covariance, and rough set algorithms for feature selection and reduction. The experiments on trees extraction from aerial images show that the methods presented in this paper are practical and effective.

An analysis on human personality and hotel web design : a Kohonen network approach

Having an eye catching and attractive website could help hotels to compete in the vigorous online market. This study attempts to examine the relationship between human personality and the web design preferences. Kohonen Networks were adopted to cluster people with similar personality characteristics and identify their differences on web design preferences. Empirical results indicated people with similar personality traits have similar design preferences. For example, to attract those who got high scores in agreeableness, conscientiousness and openness but low score in neuroticism, a web page should start with a language selection page with introductory movie, one large image on the web page showing hotel interior design with hotel guest in the photo, and with background music.