  The main focus of information retrieval methods is to provide accurate and
efficient results which are cost-effective too. LINGO (Label Induction Grouping
Algorithm) is a clustering algorithm that aims to provide search results in
form of quality clusters but also has a few limitations. In this paper, our
focus is based on achieving results that are more meaningful and improving the
overall performance of the algorithm. LINGO works on two main steps; Cluster
Label Induction by using Latent Semantic Indexing technique (LSI) and Cluster
content discovery by using the Vector Space Model (VSM). As LINGO uses VSM in
cluster content discovery, our task is to replace VSM with LSI for cluster
content discovery and to analyze the feasibility of using LSI with Okapi BM25.
The next task is to compare the results of a modified method with the LINGO
original method. The research is applied to five different text-based data sets
to get more reliable results for every method. Research results show that LINGO
produces 40-50% better results when using LSI for content Discovery. From
theoretical evidence using Okapi BM25 for scoring method in LSI (LSI+Okapi
BM25) for cluster content discovery instead of VSM, also results in better
clusters generation in terms of scalability and performance when compares to
both VSM and LSI's Results.
