Nowadays outcome‐based methods are adapted in e‐learning system to meet the need of the novel development of e‐learning systems for improved web‐based retrieval results. Typically, knowledge retrieval process is denoted by the production rule, frame, semantic network, fuzzy logic, predicate logic, and group of skill concept. To get the optimized result in proposed skill‐based e‐learning, the fuzzy knowledge model is applied. In knowledge retrieval, the fuzzy membership value of the knowledge and the combinations of framed rules are used to acquire the knowledge. The fuzzy techniques are adapted for analyzing the retrieved knowledge concept of individual skills like Inadequate (Ia), Adequate (A), Value added adequate (Vaa), and Integrated skill (I) and in fuzzy inference system in skill‐based education provides a decision about the learner community skill set and it promotes the excellence skill through the delivery of the suitable courses to the learners instead of supplying common courses to different skilled persons. In the existing knowledge modeling methods known as the Knowledge Capturing and Modeling, concept map‐based knowledge modeling confirm the learners to have known or unknown domain concept. Further, many of the researchers present on the analysis of the learner performances, behavior, learning environment, etc. The proposed paper investigate the individual skill abilities and it is suggested a set of courses in adaptive curriculum and syllabus to the learner and also it adapts andragogy in skill‐based education, to model the fuzzy knowledge, the fuzzy membership function and rules are used.

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Sequential pattern mining discovers frequent user access patterns from web logs. Apriori-like sequential pattern mining techniques requires expensive multiple scans of database.  So, now days, WAP (Web Access Pattern) tree based algorithm is used. It is faster than traditional techniques.  However, the use of conditional search strategies in WAP-tree based mining algorithms requires re-construction of large numbers of intermediate conditional WAP-trees, which is also very costly.
In this paper, Kongu Arts and Science College (KASC) web logs are taken for mining. Here, we propose an efficient sequential pattern mining techniques for KASC web log access sequences known as CS-WAP Tree.  This proposed algorithm modifies the WAP tree approach for improving efficiency. The proposed algorithm totally eliminates the need to engage in numerous reconstructions of intermediate WAP trees and considerable reduces execution time. The results of experiments show the efficiency of the improved algorithm. The next key aim is to compare WAP algorithms