![]() ![]() ![]() These classes describe the characteristics of items or represent what the data points have in common with each. Classification uses predefined classes to assign to objects.For example, association rules would search a company's sales history to see which products are most commonly purchased together with this information, stores can plan, promote, and forecast. This relationship in itself creates additional value within the data set as it strives to link pieces of data. Association rules, also referred to as market basket analysis, search for relationships between variables. ![]() A free resource from GRC Data Intelligence. Related glossary terms: decision tree, box plot However, because the concurrence of variables does not constitute information about their relationship (which could, after all, be merely coincidental), further analysis is required to yield any useful conclusions. Data dredging is sometimes used to present an unexamined concurrence of variables as if they led to a valid conclusion, prior to any such study.Īlthough data dredging is often used improperly, it can be a useful means of finding surprising relationships that might not otherwise have been discovered. To make a valid assessment of the relationship between any two variables, further study is required in which isolated variables are contrasted with a control group. Many variables may be related through chance alone others may be related through some unknown factor. Data dredging is sometimes described as "seeking more information from a data set than it actually contains."ĭata dredging sometimes results in relationships between variables announced as significant when, in fact, the data require more study before such an association can legitimately be determined. Sometimes conducted for unethical purposes, data dredging often circumvents traditional data mining techniques and may lead to premature conclusions. The traditional scientific method, in contrast, begins with a hypothesis and follows with an examination of the data. ![]() Data dredging (data fishing) - Data dredging, sometimes referred to as "data fishing" is a data mining practice in which large volumes of data are analyzed seeking any possible relationships between data. ![]()
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