Clustering is similar to classification.This data mining technique allows the underlying data to be more neatly categorized and summarized across similar features or product lines. 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.You would like to show progress, so you decide to include them in your report to management. Your company has no business in Ohio or Louisiana, and it there is no reasonable conclusion why these correlations exist, even though the p-values are less than 5%. Of the 100, two show statistical significance: interest rates on boat loans in south Louisiana and household debt in north Ohio.

You download a database from a government website and decide to run a series of automated regressions.

Imagine that instead of choosing only four macroeconomic indicators, you chose 100. This could have gone another way…īUT wait! This could have gone another way. An honest analyst would not make this claim, but show that there is no relationship. You had conflicting evidence of the statistical significance of two correlations, but since one side of the evidence supports a personal claim you would like to make, you decide to accept it. Since you’re in a bind, you decide to claim that GDP and Employment rate have a strong relationship to revenue, and that you’re going to explore this further.

- what is data dredging?