The process of clustering in the general perspective is limited to the grouping of data into clusters and finds its applications in the fields of information retrieval, text ranking and classification and more. The dimension of e-Learning is to improve learning with various tools and technologies. Grouping of learners based on their learning levels is found to improve the learning abilities. Scientific method to cluster the learners is not available in literature, which can further simplify the amalgamation of learning complemented through clustering. This paper is an attempt to examine the aspects of implementing clustering to group the learners according to their learning abilities.

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The existing semantic methods cluster the documents based on unabridged or abridged term comparisons. After clustering, these terms are not preserved, costing the cluster operation to be repeated in its entirety upon the arrival of new documents. Hence the semantic clustering methods can be considered as “on the go” methods. Re-clustering becomes unavoidable in all circumstances both in the Iterative and Incremental Clustering Methods. It would be more appropriate to build and evolve a lexicon with the derived keywords of the documents and to refer them in further cluster operations. The rationale is to deny re-clustering upon new documents and refer the Lexicon to formulate clusters until the quality of clusters is intact, and when it breaks above the threshold, the cluster operation can be repeated. Since re-clustering is delayed until a breakeven point, the process of re-clustering becomes faster. This process may incur additional runtime complexity, but would extremely simplify and speed up the process of re-clustering. This paper discusses about the construction of lexicons and its applications in clustering. The Keyword based Lexicon Construction Algorithm (KBLCA) is demonstrated to build lexicons and the breakeven point for re-clustering is proposed and described. The theory of denying re-clustering is briefed, along with experimental results.