Clustering User Access Patterns based on Fuzzy Rough k-Means
基于模糊粗糙k-均值的用户访问模式的聚类

The interest of web users can be revealed by their visited web pages and time durations on these web pages during their susfing.In order that similarity/difference between any two patterns can be easily gained,each web access pattern from web logs is transformed as fuzzy vector with same length,in which each element is a fuzzy linguistic variable or 0 representing the visited web page and time duration on this web page.The clusters may not exist crisp boundaries,thus a rough k-means clustering algorithm is proposed to group the fuzzy vectors denoting users' surfing behaviors.Finally,Davies-Bouldin index is provided to measure the clustering exactness.