Space Data

Data Mining in Finance presents a comprehensive overview of major algorithmic approaches to predictive data mining, including statistical, neural networks, ruled-based, decision-tree, and fuzzy-logic methods, and then examines the suitability of these approaches to financial data mining. The book focuses specifically on relational data mining (RDM), which is a learning method able to learn more expressive rules than other symbolic approaches. RDM is thus better suited for financial mining, because it is able to make greater use of underlying domain knowledge. Relational data mining also has a better ability to explain the discovered rules - an ability critical for avoiding spurious patterns which inevitably arise when the number of variables examined is very large. The earlier algorithms for relational data mining, also known as inductive logic programming (ILP), suffer from a relative computational inefficiency and have rather limited tools for processing numerical data.

This hard disk analysis, file management and disk space reporting software. It allows you to view disk space usage by NTFS owner on a selected volume or directory. You can also view disk space usage by shared folder or quantity of objects. The program allows you to search folders and files by size or NTFS owners, and you can export file system data to a text file.

Learn to Measure Direct Marketing Success with Statistical Methods in this Course

This title provides a comprehensive, unified tutorial covering the most recent advances in the emerging technology of free-space optics (FSO), a field in which interest and attention continue to grow along with the number of new challenges.

SQL, Snowflake and Data Warehouse

The Handbook of Dynamic Driven Applications Systems establishes an authoritative reference of DDDAS, pioneered by Dr. Darema and the co-authors for researchers and practitioners developing DDDAS technologies.