Identify website personality by using unsupervised learning based on quantitative website elements

Abstract

This paper reports a pilot study in identifying and ranking the personality of a website automatically and intelligently to help the users to find a more suitable website and to help the owners to improve the quality of their websites. The mapping between the selected items defined in WPS and the quantitative elements of a website was developed first. 240 valid websites were classified by using unsupervised clustering algorithm K-means. The classification was implemented for multiple times from K = 2 to K = 15. The average values for each attribute in each cluster were calculated, the standard deviation for all the clusters for a given K value was calculated to find out a suitable K value. A preliminary verification suggested that the attributes and the method used can properly identify the personality of a website. A software written in Java integrating other existing software packages was developed for the required experiments.