Fuzzy Based DBSCAN Text Mining Technique for Malware Detection

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Surabhi Thapa
Neena Madan

Abstract

Text mining is an approach to perceive or find interesting and useful relationships and patterns in considerable amount of text. The Density based spatial clustering of applications with noise (DBSCAN) clustering is a vital method in data mining. DBSCAN was projected to adopt density-reachability and density attachment for handling the randomly shaped clusters and noise. It has been perceived that in existing work has introduced method persists until the density-linked cluster is completely discovered. Then, a new obscure element is extracted and processed, leading to the finding of a further cluster or noise. Therefore this paper has proposed fuzzy based DBSCAN text mining technique for malware detection that enhances ambiguity and functioning of the clusters formed. The experimental outcomes bring about the suggested method that clearly shows the fact that suggested method outperforms over the existing methods.

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Author Biography

Neena Madan

Department of Computer Science & Engineering, Guru Nanak Dev University, Regional Campus, Jalandhar, India