  Biclustering is a two way clustering approach involving simultaneous
clustering along two dimensions of the data matrix. Finding biclusters of web
objects (i.e. web users and web pages) is an emerging topic in the context of
web usage mining. It overcomes the problem associated with traditional
clustering methods by allowing automatic discovery of browsing pattern based on
a subset of attributes. A coherent bicluster of clickstream data is a local
browsing pattern such that users in bicluster exhibit correlated browsing
pattern through a subset of pages of a web site. This paper proposed a new
application of biclustering to web data using a combination of heuristics and
meta-heuristics such as K-means, Greedy Search Procedure and Genetic Algorithms
to identify the coherent browsing pattern. Experiment is conducted on the
benchmark clickstream msnbc dataset from UCI repository. Results demonstrate
the efficiency and beneficial outcome of the proposed method by correlating the
users and pages of a web site in high degree.This approach shows excellent
performance at finding high degree of overlapped coherent biclusters from web
data.
