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What is descriptive data mining?

Descriptive Data Mining Models. This chapter describes descriptive models, that is, the unsupervised learning functions. These functions do not predict a target value, but focus more on the intrinsic structure, relations, interconnectedness, etc. of the data.

What is a data mining model?

The mining model is more than the algorithm or metadata handler. It is a set of data, patterns, statistics that can be serviceable on new data that is being sourced to generate the predictions and get some inference about the relationships. The following are some of the techniques that are used in data mining. 1. Descriptive Data Mining Technique

What is a descriptive model?

A descriptive model, on the other hand, is describing the data in a form that allows for future action strategies, but it is not a precise event. Rather, it is a perspective into large quantities of data, so business can make sense of the data. It describes data in clusters or association rules so it doesn’t need to be accurate, just approximate.

What is data mining using classification?

Data mining using classification usually involves a testing phase to check how good the model is. For this, data where the outcome is known is tested to see how well the model's predictions match it. For instance, you would take data for customers who have bought a digital camera in the past and check it against the predictions given by the model.

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