  Community identification algorithms have been used to enhance the quality of
the services perceived by its users. Although algorithms for community have a
widespread use in the Web, their application to portals or specific subsets of
the Web has not been much studied. In this paper, we propose a technique for
local community identification that takes into account user access behavior
derived from access logs of servers in the Web. The technique takes a departure
from the existing community algorithms since it changes the focus of in terest,
moving from authors to users. Our approach does not use relations imposed by
authors (e.g. hyperlinks in the case of Web pages). It uses information derived
from user accesses to a service in order to infer relationships. The
communities identified are of great interest to content providers since they
can be used to improve quality of their services. We also propose an evaluation
methodology for analyzing the results obtained by the algorithm. We present two
case studies based on actual data from two services: an online bookstore and an
online radio. The case of the online radio is particularly relevant, because it
emphasizes the contribution of the proposed algorithm to find out communities
in an environment (i.e., streaming media service) without links, that represent
the relations imposed by authors (e.g. hyperlinks in the case of Web pages).
