Web data mining and applications in business intelligence and counter terrorism pdf
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- 12 Most Useful Data Mining Applications of 2021
- Mining Web Databases for Counter-Terrorism | Taylor
- Top 14 useful applications for data mining
12 Most Useful Data Mining Applications of 2021
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As the importance of data analytics continues to grow, companies are finding more and more applications for Data Mining and Business Intelligence. Here we take a look at 5 real life applications of these technologies and shed light on the benefits they can bring to your business. In order for data to really be valuable to an organization, you need to be able to discover patterns and relationships within that data. Those connections and insights can enable better business decisions. Data mining can also reduce risk, helping you to detect fraud, errors, and inconsistencies that can lead to profit loss and reputation damage.
With data mining , a retailer can use point-of-sale records of customer purchases to develop products and promotions to appeal to specific customer segments. Data mining holds great potential to improve health systems. It uses data and analytics to identify best practices that improve care and reduce costs. Researchers use data mining approaches like multi-dimensional databases, machine learning, soft computing, data visualization and statistics. Mining can be used to predict the volume of patients in every category. Processes are developed that make sure that the patients receive appropriate care at the right place and at the right time. Data mining can also help healthcare insurers to detect fraud and abuse.
Mining Web Databases for Counter-Terrorism | Taylor
Data mining is a method of extracting data from multiple sources and organizing it to derive valuable insights. Read on to discover the wide-ranging data mining applications that are changing the industry as we know it! Modern-day companies cannot live in a data lacuna. They have to evolve and keep up with technological evolution and upcoming digital trends to stay ahead of the competition. So, businesses today are prioritizing staying abreast of all the new developments in the field of data science and analytics.
It seems that you're in Germany. We have a dedicated site for Germany. The University of Arizona Artificial Intelligence Lab AI Lab Dark Web project is a long-term scientific research program that aims to study and understand the international terrorism Jihadist phenomena via a computational, data-centric approach. We aim to collect "ALL" web content generated by international terrorist groups, including web sites, forums, chat rooms, blogs, social networking sites, videos, virtual world, etc. We have developed various multilingual data mining, text mining, and web mining techniques to perform link analysis, content analysis, web metrics technical sophistication analysis, sentiment analysis, authorship analysis, and video analysis in our research. The approaches and methods developed in this project contribute to advancing the field of Intelligence and Security Informatics ISI. Such advances will help related stakeholders to perform terrorism research and facilitate international security and peace.
Top 14 useful applications for data mining
Data mining is a process of discovering patterns in large data sets involving methods at the intersection of machine learning , statistics , and database systems. The term "data mining" is a misnomer , because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction mining of data itself. The book Data mining: Practical machine learning tools and techniques with Java  which covers mostly machine learning material was originally to be named just Practical machine learning , and the term data mining was only added for marketing reasons. The actual data mining task is the semi-automatic or automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records cluster analysis , unusual records anomaly detection , and dependencies association rule mining , sequential pattern mining.
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