An improved differential bond energy algorithm with fuzzy merging method to improve the document clustering for information mining

Abstract

AbstractThe vast and diversified text materials on the internet in recent years have drastically increased the importance of information mining. By organizing documents into cohesive groupings, a document clustering method is a suitable tool for dealing with massive amounts of documents. Text documents, on the other hand, include sparse and uninformative features like noise, unrelated, and unneeded features, which reduce the efficiency of the document clustering methods. For noise removal, this work employs the hyperclique pattern‐based data cleaner pre‐processing approach. Then, differential bond energy algorithm (DBEA) is combined with a fuzzy merging approach and termed improved differential bond energy algorithm with fuzzy merging (IDBEFM) to handle the issues present in text document clustering. It seeks to discover and display natural variable clusters among large amounts of data. IDBEFM clusters are documented in three steps: in the first step, a cluster similarity matrix is instantiated by applying the DBEA. The second step develops an innovative technique for automatically dividing the cluster matrix into compact cohesive clusters, and in the third step, a fuzzy merging approach is used for combining identical clusters by the correlations and interrelationships between the resultant clusters. The test findings demonstrated that the accomplishment of the proposed technique is much superior to standard clustering algorithms.