![]() We articulate case studies (from a data science perspective) in aspects Gain a comprehensive understanding of the difficulties in mining unusual domains, which include both illicit domains e.g., human trafficking and securities fraud, as well as non-illicit domains not studied too often but with immense social utility e.g., humanitarian and disaster relief. In this tutorial, we provide an overview, using demos, examples and case studies, of the research landscape for data mining in unusual domains, including recent work that has achieved state-of-the-art results in constructing knowledge graphs in a variety of unusual domains, followed by inference and search using both command line and graphical interfaces. ![]() Data mining in such domains has the potential for widespread social impact, and is also very challenging technically. Notable examples of unusual Web domains include both illicit domains, such as human trafficking advertising, illegal weapons sales, counterfeit goods transactions, patent trolling and cyberattacks, and also non-illicit domains such as humanitarian and disaster relief. In particular, such domains have significant long tails and exhibit concept drift, and are characterized by high levels of heterogeneity. There are several factors that make a domain unusual. Data mining over Web domains that are unusual is an even harder problem. ![]() The growth of the Web is a success story that has spurred much research in knowledge discovery and data mining.
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