Most programmers and users resort to find individual solution per problem depending on the data and nature of problem, that will lead to solve a specific problem using an algorithm without the ability of this solution to solve a new problem. This variance comes from the difference in algorithm parameters from one problem to another, as these parameters related to data nature, its size, and values it carried that can affect the way algorithm work. Individual solutions lead to increase in time cost and effort spent on solving a new problem, which the new problem requires to work on programming new criteria for algorithm solution. That is prompted us to highlight necessaries to develop main components for algorithms used in practical life, such as data mining algorithms so that a solution designed for one problem can be more easily adapted to new problems with different data structures, within the general scope of decision tree applicability. These algorithm components need control mechanism settings, so when using component to solve problem, there is no need to develop algorithm settings again, regardless data size and data structure. We found that the dynamic solution saves effort and time needed to solve problems with same algorithm. In this paper, we present our methodology for using ID3 decision tree algorithm to mine data dynamically, and the mechanism used to achieve the dynamic solution, that provides a flexible and reusable solution for a wide range of problems that require decision tree support, reducing the need to redesign or reimplement models for each new task The proposed model was tested on three datasets. The proposed model achieved an accuracy of 97%, 97%, and 93% on the breast cancer, heart disease, and diabetes datasets, respectively.

In the last two decades, illicit activities have dramatically increased on the Dark Web. Every year, Dark Web witnesses establishing new markets, in which administrators, vendors, and consumers aim to illegal acquisition and consumption. On the other hand, this rapid growth makes it quite difficult for law and security agencies to detect and investigate all those activities with manual analyses. In this paper, we introduce our approach of utilizing data mining techniques to produce useful patterns from a dark web market contents. We start from a brief description of the methodology on which the research stands, then we present the system modules that perform three basic missions: crawling and extracting the entire market data, data pre-processing, and data mining. The data mining methods include generating Association Rules from products’ titles, and from the generated rules, we infer conceptual compositions vendors use when promoting their products. Clustering is the second mining aspect, where the system clusters vendors and products. From the generated clusters, we discuss the common characteristics among clustered objects, find the Top Vendors, and analyze products promoted by the latter, in addition to the most viewed and sold items on the market. Overall, this approach helps in placing a dark website under investigation.