Enhanced Document Clustering using K-Means with Support Vector Machine (SVM) Approach

Abstract

Today’s digital world consists of a large amount of data. Volume of data in the digital world is increasing continuously. Dealing with such important, complex and unstructured data is important. These files consist of data in unstructured text, whose analysis by computer examiners is difficult to be performed. In forensic analysis, experts have to spend a lot of time as well as efforts, to identify criminals and related evidence for investigation. However crime investigation process needs to be faster and efficient. As large amount of information is collected during crime investigation, data mining is an approach which can be useful in this perspective. Data mining is a process that extracts useful information from large amount of crime data so that possible suspects of the crime can be identified efficiently. Algorithms for clustering documents can provide the learning of knowledge from the documents under analysis. This can be done by applying different clustering algorithms to different datasets. Clustering algorithms indeed tends to induce clusters formed by either relevant or irrelevant documents, further extending work by using Clustering Technique Cascaded with Support Vector Machine, thus contributing to enhance the experts job and investigation process can be speed up.
DOI: 10.17762/ijritcc2321-8169.1506126