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The difference between data analysis and data mining is that data analysis is used to test models and hypotheses on the dataset, e.g., analyzing the effectiveness of a marketing campaign, regardless of the amount of data. In contrast, data mining uses machine learning and statistical models to uncover clandestine or hidden patterns in a large ...
Environmental data is typically generated by institutions executing environmental law or doing environmental research. Environment statistics are usually generated by statistical offices and are considered as environmental data, too. Socio-economic data and other statistical data (often the "D" and the "R" of the DPSIR model) are not considered ...
Spatial data mining is the application of data mining methods to spatial data. The end objective of spatial data mining is to find patterns in data with respect to geography. So far, data mining and Geographic Information Systems (GIS) have existed as two separate technologies, each with its own methods, traditions, and approaches to ...
Affinity analysis falls under the umbrella term of data mining which uncovers meaningful correlations between different entities according to their co-occurrence in a data set. In almost all systems and processes, the application of affinity analysis can extract significant knowledge about the unexpected trends [citation needed]. In fact ...
Reality mining is the collection and analysis of machine-sensed environmental data pertaining to human social behavior, with the goal of identifying predictable patterns of behavior. In 2008, MIT Technology Review called it one of the "10 technologies most likely to change the way we live."
Data farming is the process of using designed computational experiments to “grow” data, which can then be analyzed using statistical and visualization techniques to obtain insight into complex systems. These methods can be applied to any computational model. Data farming differs from Data mining, as the following metaphors indicate:
A data product is a computer application that takes data inputs and generates outputs, feeding them back into the environment. [41] It may be based on a model or algorithm. For instance, an application that analyzes data about customer purchase history, and uses the results to recommend other purchases the customer might enjoy.
OLAP is part of the broader category of business intelligence, which also encompasses relational databases, report writing and data mining. [3] Typical applications of OLAP include business reporting for sales, marketing, management reporting, business process management (BPM), [4] budgeting and forecasting, financial reporting and similar ...