Web Usage Mining

Abstract

With the continued growth and proliferation of e-commerce, Web services, and Web-based information systems, the volumes of clickstream, transaction data, and user profile data collected by Web-based organizations in their daily operations has reached astronomical proportions. Analyzing such data can help these organizations determine the lifetime value of clients, design cross-marketing strategies across products and services, evaluate the effectiveness of promotional campaigns, optimize the functionality of Web-based applications, provide more personalized content to visitors, and find the most effective logical structure for their Web space. This type of analysis involves the automatic discovery of meaningful patterns and relationships from a large collection of primarily semi-structured data, often stored in Web and applications server access logs, as well as in related operational data sources. Web usage mining refers to the automatic discovery and analysis of patterns in clickstreams, user transactions and other associated data collected or generated as a result of user interactions with Web resources on one or more Web sites [28, 82, 118]. The goal is to capture, model, and analyze the behavioral patterns and profiles of users interacting with a Web site. The discovered patterns are usually represented as collections of pages, objects, or resources that are frequently accessed or used by groups of users with common needs or interests. Following the standard data mining process [38], the overall Web usage mining process can be divided into three interdependent stages: data collection and pre-processing, pattern discovery, and pattern analysis.