In todays digital world, the forensic analysis is of great importance. Huge amount of data has been examined by analyst to present evidences in court. But, usually the files in those computers consist of data is in unstructured form. Thus, it is very difficult to analyze such data. To overcome this difficulty, the automatic analysis of data is of great interest. The algorithms for automatic clustering can be used to retrieve the interesting knowledge and useful information from the data which is unstructured and unorganized. We will propose the algorithm for clustering of data in automatic manner useful for computer forensic experts for the analysis of data. We will experiment for such things by proposing an approach of enhanced K-medoid algorithm with representatives over well known clustering algorithms. We performed experiment on the data collected from different real time crime data sources found in Police investigation FIRs’. We will propose the enhanced preprocessing techniques which can be beneficial over the well known stemmer algorithms. Finally, we summarize the results using good visualization techniques.
Cite this article:
Rahul D. Kopulwar, Chetan Bawankar. Enhanced Clustering for Forensic Analysis. Research J. Engineering and Tech. 6(3): July- Sept., 2015 page 374-376. doi: 10.5958/2321-581X.2015.00058.6
Rahul D. Kopulwar, Chetan Bawankar. Enhanced Clustering for Forensic Analysis. Research J. Engineering and Tech. 6(3): July- Sept., 2015 page 374-376. doi: 10.5958/2321-581X.2015.00058.6 Available on: https://ijersonline.org/AbstractView.aspx?PID=2015-6-3-11