The beginning of the 21st century has witnessed the generation of spectacular amounts of new information, ranging from marketing data to genomic sequences. As traditional statistical methods are gradually being defeated by both the amount of data and the general absence of underlying hypotheses, data mining procedures are becoming increasingly popular and user-friendly. By combining statistical-, artificial intelligence- and database management tools, those methods are tailored for processing large quantities of information and extracting interesting patterns. Since their first application, data mining procedures have progressively been tweaked to accommodate various types of information, including social science- and biological data. However, a number of features characteristic of biological data, including high levels of measurement variability and correlation between variables, represent an additional challenge and call for specific methods. The goal of this editorial is to highlight the spatial dimension of biological data mining.
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The spatial dimension in biological data mining
Semantic Scholar · Biology · 2011
Abstract
The beginning of the 21st century has witnessed the generation of spectacular amounts of new information, ranging from marketing data to genomic sequences. As traditional statistical methods are gradually being defeated by both the amount of data and the general absence of underlying hypotheses, data mining procedures are becoming increasingly popular and user-friendly. By combining statistical-, artificial intelligence- and database management tools, those methods are tailored for processing large quantities of information and extracting interesting patterns. Since their first application, data mining procedures have progressively been tweaked to accommodate various types of information, including social science- and biological data. However, a number of features characteristic of biological data, including high levels of measurement variability and correlation between variables, represent an additional challenge and call for specific methods. The goal of this editorial is to highlight the spatial dimension of biological data mining.