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Semantic data mining is a subset of data mining that specifically seeks to incorporate domain knowledge, such as formal semantics, into the data mining process.Domain knowledge is the knowledge of the environment the data was processed in. Domain knowledge can have a positive influence on many aspects of data mining, such as filtering out redundant or inconsistent data during the preprocessing ...
The term data mining appeared around 1990 in the database community, with generally positive connotations. For a short time in 1980s, the phrase "database mining"™, was used, but since it was trademarked by HNC, a San Diego–based company, to pitch their Database Mining Workstation; [11] researchers consequently turned to data mining.
This count, either as a ratio of the total or normalized by dividing by the expected count for a random source model, is known as the index of coincidence, or IC or IOC [2] or IoC [3] for short. Because letters in a natural language are not distributed evenly , the IC is higher for such texts than it would be for uniformly random text strings.
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 ...
In a classification task, the precision for a class is the number of true positives (i.e. the number of items correctly labelled as belonging to the positive class) divided by the total number of elements labelled as belonging to the positive class (i.e. the sum of true positives and false positives, which are items incorrectly labelled as belonging to the class).
Daimler-Benz had a significant data mining team. OHRA was starting to explore the potential use of data mining. The first version of the methodology was presented at the 4th CRISP-DM SIG Workshop in Brussels in March 1999, [5] and published as a step-by-step data mining guide later that year. [6]
Considering all the above-mentioned definitions, affinity analysis can develop rules that will predict the occurrence of an event based on the occurrence of other events. This data mining method has been explored in different fields including disease diagnosis, market basket analysis, retail industry, higher education, and financial analysis.
These algorithms are designed to operate with limited memory, generally logarithmic in the size of the stream and/or in the maximum value in the stream, and may also have limited processing time per item. As a result of these constraints, streaming algorithms often produce approximate answers based on a summary or "sketch" of the data stream.