RELATED WORK
Fields like artificial intelligence, web mining and pattern recognition are influenced by many factors. Classification is one such important factor. Heuristic searching techniques forms the basis for majority of common classification algorithms. Finding the subset of the representative rules from the restricted set of training data is the main role of these algorithms [20,21].
Fundamentally there are three objectives: generating user navigation profiles for link prediction; enriching the profiles with semantic information to diversify them, and obtaining global and language-dependent user interest profiles combined with web usage and content mining techniques [22]. In this paper [22], the website content and the standard web usage information (log files from the web server) are taken as input. Semantic structure is automatically extracted and combined with information sources to extract knowledge for many applications. A crispbased approach is used to assign the interest profiles to clusters. User’s interests change dynamically during web traversal. Hence it is essential to detect the dynamic web traversal patterns based upon the user’s progress. TFPM (Transitional Frequent Pattern Mining) and TUPM (Transitional Utility Pattern Mining) were proposed to detect the web patterns [23].
Analysing students’ study environment to suggest personalised activities for each student [24] is another remarkable work in this area. The recommended suggestions were made to reinforce the students’ competences in a subject. Characteristics of competences from each subject and the designed activities modelled by fuzzy linguistic labels compute the entire recommendations. A recommendation system based on fuzzy linguistic modelling has this approach as its core. And this approach allows students to receive personalised activities to practice competences which have to reinforce for passing a subject. The unhandled issue is that it is very difficult to determine