Image Analysis and Image Mining Techniques: A Review

This paper presents the analysis of existing literature which is relevant to mining and the mechanisms associated with weighted substructure. Though, the literature consists of a lot many research contributions, but, here, we have analysed around thirty-five research and review papers. The existing approaches are categorized based on the basic concepts involved in the mechanisms. The emphasis is on the concept used by the concerned authors, the database used for experimentations and the performance evaluation parameters. Their claims are also highlighted. Our findings from the exhaustive literature review are mentioned along with the identified problems. This paper is useful for comparative study of various approaches which is prerequisite for solving image mining problem

Data visualisation in digital forensics

As digital crimes have risen, so has the need for digital forensics. Numerous state-of-the-art tools have been developed to assist digital investigators conduct proper investigations into digital crimes. However, digital investigations are becoming increasingly complex and time consuming due to the amount of data involved, and digital investigators can find themselves unable to conduct them in an appropriately efficient and effective manner. This situation has prompted the need for new tools capable of handling such large, complex investigations. Data mining is one such potential tool. It is still relatively unexplored from a digital forensics perspective, but the purpose of data mining is to discover new knowledge from data where the dimensionality, complexity or volume of data is prohibitively large for manual analysis. This study assesses the self-organising map (SOM), a neural network model and data mining technique that could potentially offer tremendous benefits to digital forensics. The focus of this study is to demonstrate how the SOM can help digital investigators to make better decisions and conduct the forensic analysis process more efficiently and effectively during a digital investigation. The SOM’s visualisation capabilities can not only be used to reveal interesting patterns, but can also serve as a platform for further, interactive analysis.Dissertation (MSc (Computer Science))--University of Pretoria, 2007.Computer Scienceunrestricte