The majority of users realize the Internet information extraction from search engines or Web browsers. These search engines do not necessarily return the information users want, both in terms of volume and in terms of content. The concept of “web mining” or “data mining of Web” can be defined as the process of discovery and analysis of useful information from the data originated. Includes three types of information: data in Internet; data “log” of Internet access servers, user registration, profiles, etc.; and web structure data. In the case of web mining content, the goal is to identify “patterns” of behavior and extract knowledge from a set of data related to documents (text, image, audio, video, etc.) stored in tables within a web environment. In the case of web data, unstructured documents with different attributes which may have similar semantics in the context of web information. The knowledge discovery “hidden” on the Internet is one of the major features of the process “web mining”. This discovered knowledge can be very useful to decision makers, helping them to identify abnormal or unknown behavior in the use or the content of the Internet [1]. Currently, the use of the term “science of data” is increasingly common, as well as the term “big data.” Here “science of data” is the study of the data knowledge extraction (heterogeneous and unstructured – texts, images and videos from networks with complex relationships between its entities). As examples, Paypal and Google use predictive models to supported business on the Internet [2]. In the context of this study, we used Google to search the set of URLs (Universal Resource Locator) and corresponding sites summaries with one or more words, particularly about the City and/or State “Rio de Janeiro” (Brazil) followed by other attributes. Depending on the research that takes place, the result can be a significant amount of URLs arranged under a “ranking” (“PageRank”, in the case of Google). This “ranking” indicates the most searched sites (quantity and quality) in a descending order according seeker’s own criteria [3], [4]. For the result of this search was as effective as possible, this study was guided then, the following research question: “How to identify patterns (or rules) in the Web mining under Multi-criteria approach?”. The choice of the Rough Set Theory (RST) and the Dominance principle (Dominance-based Rough Set Approach, DRSA) as tools to support Multi-criteria decision justified by the possibility of inaccurate data (inconsistent) and the need for treatment of these inaccuracies; and the ability to process an information system (or data table) in a mathematical perspective as well, do not need a data history as required by Fuzzy Sets (Fuzzy Set Theory) proposed by Lotfi Asker Zadeh in 1965 [5]. As support for Multi-criteria analysis, we used the jMAF software (Dominance-based Rough Set Data Analysis Framework) [6], given for purposes of research at the Computer Science Institute, Poznan University of Technology, Poland. This study includes a brief approach on Rough Set Theory (RST) and the Dominance principle (DRSA) – Sections 2 and 3, respectively; the application of the Dominance principle to a specific case, Section 4; and ends with the conclusions and directions for future studies, Section 5.