Abstract: Data mining, which is also referred to as knowledge discovery in databases, is the process of extracting valid previously unknown, comprehensible and actionable information from large ...

Learn about the most promising data mining techniques for the future, such as deep learning, graph mining, reinforcement learning, ... and by enabling relational and semantic analysis.

Data perturbation is deemed a common problem in data processing. It is often inevitable to avoid noisy or misleading data which may arise from real-world collection or model imprecision. Besides, when ...

Analysis of the data includes simple query and reporting, statistical analysis, more complex multidimensional analysis, and data mining. The information frequently is stored in a data warehouse, a ...

Learn the key differences between relational and NoSQL databases with this in-depth comparison. There’s nothing wrong with the traditional relational database management system. In fact, many ...

Police are getting better tools for mining data. They're supposed to make law enforcement more surgical, but some say it's a high-tech justification for targeting certain places and people.

The underlying relational database was designed to allow flexibility in the nature ... data mining tools are essential to fully exploit the power of microarray expression analysis. Data mining ...

The Core Dataset(s) from which the unit of analysis and/or population of interest will be identified; The reference period for the unit of analysis and/or population of interest; 3. Geography. The ...

Step 1: Handling of incomplete data. Incomplete data affects classification accuracy and hinders effective data mining. The following techniques are effective for working with incomplete data.