Paper Info
Title
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.
Year
DOI
Venue
2015
10.1007/978-3-319-26532-2_57
Lecture Notes in Computer Science
Keywords
Field
DocType
Website,Unsupervised learning,K-means,Data extraction,Experiments,Ranking,Personality,Classification
Data mining,k-means clustering,Information retrieval,Ranking,Computer science,Software,Unsupervised learning,Data extraction,Cluster analysis,Java,Personality
Conference
Volume
ISSN
Citations 
9489
0302-9743
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Shafquat Chishti100.34
Xiaosong Li252.88
Abdolhossein Sarrafzadeh313422.64