Analysis of click stream allows the derivation of crucial intelligence aimed at guiding the maintenance, support, and evolution of websites, thereby addressing their primary objective of guaranteeing that the community of users efficiently exploits the underlying information space. This is of pivotal importance when considering that, contrary to other media, the cost of publication and transformation of information or knowledge in material hosted on the web is virtually zero.

AI for Clickstream Data Analysis  is unfolding and understanding users’ complicated behaviors using large datasets collected from internet browsing and site navigation. In order to understand the behavior of users while browsing the web, it is essential to understand the concept of data mining. Data mining is a technique where researchers mine the data thoroughly and try to present the user behavior based on facts collected from clickstream data.

Data mining is of great importance for researchers because of the following reasons. Clickstream data is one of the most essential datasets in understanding users browsing the web. Every day, millions of users submit billions of requests while browsing the web. Various service providers discipline these user requests and store them in huge clickstream datasets.

• Data Collection

Any request on the part of the user to cause a new display to be shown can be considered to be a page transition. On a website, however, numerous requests need to be followed by showing a new page. For video or audio to be heard, a request may trigger the display of associated related content within the same shell. Variously timed requests that seek to pursue the same navigation path as has already been navigated should also be excluded.

Decisions have to be made, and questions have to be answered regarding the collection of clickstream data. The action that triggers clickstream recording can be of different types. A multitude of techniques for clickstream data collection can be applied, and there are several options for making clickstream data available for analysis.

• Data Preprocessing

In general, the research attempts to uncover effective methods for combining clickstream data information with concepts that are utilized in their traditional counterparts. Clickstream data analysis is an effective method for e-commerce websites to track where and how site visitors come to visit the sites. Recently, clickstream trend data can influence management’s judgment and further assist companies in predicting web user behavior. Clickstream trend data provides valuable information to organizations that conduct their business over the Internet. The data combines data mining techniques with current concepts from the fields of data warehousing and traffic engineering, which use website data to help businesses understand and link their information to their users.

• Applications

The first application refers to the use of clickstreams to understand users’ behavior and participative structured navigation: the sequence of previously visited pages and interaction with the five previously viewed items can be used to predict future user behavior. The applications of clickstream data analysis or AI for Clickstream Data Analysis take advantage of three critical aspects related to users’ activities with a website: clickstreams enable the understanding of users’ website navigation; website user customization is facilitated when knowledge about the user’s profile is available; and site personalization and user profiling must respect the user’s privacy.