Tham khảo tài liệu 'cookbook modeling data for marketing_10', khoa học xã hội, kinh tế chính trị phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | Page 311 through the pages of a Web site. In mapping the physical layout of a Web site a graph s nodes can represent Web pages and the directed edges can indicate hypertext links between pages. Graphs can be used to represent other navigational characteristics of a Web site for example edges can indicate the number of users that link to one page from another. Alternatively navigation-content transactions or user sessions can be used for path analysis. This type of analysis is helpful in determining the most frequently visited paths in a Web site. Because many visitors do not generally browse further than four pages into a Web site the placement of important information within the first four pages of a site s common entry points is highly recommended. Association Rules Association rule techniques are generally applied to databases of transactions where each transaction consists of a set of items. It involves defining all associations and corelations among data items where the presence of one set of items in a transaction implies the presence of other items. In the context of Web data mining association rules discover the relations among the various references made to the server files by a given client. The discovery of association rules in an organization s typically very large database of Web transactions can provide valuable input for site restructuring and targeted promotional activities. Sequential Patterns Sequential pattern analysis can be used to discover temporal relationships among data items as in for example similar time sequences for purchase transactions. Because a single user visit is recorded over a period of time in Web server transaction logs sequential pattern analysis techniques can be implemented to determine the common characteristics of all clients that visited a particular page or a sequence of pages within a certain time period. E-retailers can then combine these results with information from traditional transactional databases to predict .
