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Discovering Knowledge in Data: An Introduction to Data Mining, 2nd Edition by Daniel T. Larose

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Chapter 1An Introduction to Data Mining

  1. 1.1 What is Data Mining?
  2. 1.2 Wanted: Data Miners
  3. 1.3 The Need for Human Direction of Data Mining
  4. 1.4 The Cross-Industry Standard Practice for Data Mining
  5. 1.5 Fallacies of Data Mining
  6. 1.6 What Tasks Can Data Mining Accomplish?
    1. References
    2. Exercises

1.1 What is Data Mining?

The McKinsey Global Institute (MGI) reports [1] that most American companies with more than 1000 employees had an average of at least 200 terabytes of stored data. MGI projects that the amount of data generated worldwide will increase by 40% annually, creating profitable opportunities for companies to leverage their data to reduce costs and increase their bottom line. For example, retailers harnessing this “big data” to best advantage could expect to realize an increase in their operating margin of more than 60%, according to the MGI report. And healthcare providers and health maintenance organizations (HMOs) that properly leverage their data storehouses could achieve $300 in cost savings annually, through improved efficiency and quality.

The MIT Technology Review reports [2] that it was the Obama campaign's effective use of data mining that helped President Obama win the 2012 presidential election over Mitt Romney. They first identified likely Obama voters using a data mining model, and then made sure that these voters actually got to the polls. The campaign also used a separate data mining model to predict the polling outcomes county-by-county. In the important swing ...

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