A New Approach for Improving Computer Inspections by using Fuzzy Methods for Forensic Data Analysis

Now a day2019;s digital world data in computers has great significance and this data is extremely critical in perspective for upcoming position and learn irrespective of different fields. Therefore we the assessment of such data is vital and imperative task. Computer forensic analysis a lot of data there in the digital campaign is study to extract data and computers consist of hundreds of thousands of files which surround shapeless text or data here clustering algorithms is of plays a great interest. Clustering helps to develop analysis of documents under deliberation. This document clustering analysis is extremely useful to analyze the data from seized devices like computers, laptops, hard disks and tablets etc. There are total six algorithms used for clustering of documents like K-means, K-medoids, single link, complete link, Average Link and CSPA. These six algorithms are used to cluster the digital documents. Existing document clustering algorithms are operated in single document at a time. In the proposed approach of these working algorithm applied on multiple documents at a time. Now we using clustering technique named as agglomerative hierarchical clustering which gives better finer clusters compared to existing techniques

Automatic Labelling and Document Clustering for Forensic Analysis

In computer forensic analysis, retrieved data is in unstructured text, whose analysis by computer examiners is difficult to be performed. In proposed approach the forensic analysis is done very systematically i.e. retrieved data is in unstructured format get particular structure by using high quality well known algorithm and automatic cluster labelling method. Indexing is performed on txt, doc, and pdf file which automatically estimate the number of clusters with automatic labelling to it. In the proposed approach DBSCAN algorithm and K-mean algorithm are used; which makes it very easy to retrieve most relevant information for forensic analysis also the automated methods of analysis are of great interest. In particular, algorithms for clustering documents can facilitate the discovery of new and useful knowledge from the documents under analysis. Two methods are used for document clustering for forensic analysis; the first method uses an x2 test of significance to detect different word usage across categories in the hierarchy which is well suited for testing dependencies when count data is available. The second method selects words which both occur frequently in a cluster and effectively discriminate the given cluster from the other clusters. Finally, we also present and discuss several practical results that can be useful for researchers of forensic analysis

Informational Paradigm, management of uncertainty and theoretical formalisms in the clustering framework: A review

Fifty years have gone by since the publication of the first paper on clustering based on fuzzy sets theory. In 1965, L.A. Zadeh had published “Fuzzy Sets” [335]. After only one year, the first effects of this seminal paper began to emerge, with the pioneering paper on clustering by Bellman, Kalaba, Zadeh [33], in which they proposed a prototypal of clustering algorithm based on the fuzzy sets theory