Top 10 Data Mining Mistakes

Top 10 Data Mining Mistakes

Cover

It has been said that good judgment comes through experience, but experience seems to come through bad judgment! In two decades of mining data from diverse fields, we have made many mistakes, which may yet lead to wisdom. In the following paper, we briefly describe, and illustrate from examples, what we believe are the “Top 10” mistakes of data mining, in terms of frequency and seriousness. Most are basic, though a few are subtle. All have, when undetected, left analysts worse off than if they’d never looked at their data.

After compiling the list, we realized that an even more basic problem-mining without (proper) data must be addressed as well. So, numbering like a computer scientist (with an overflow problem), here are mistakes 0 to 10.

Vendor:
SAS
Posted:
07 Apr 2010
Published
07 Apr 2010
Format:
PDF
Length:
23 Page(s)
Type:
White Paper
Language:
English
Already a Bitpipe member? Login here

Download this White Paper!

By submitting you agree to receive email from TechTarget and its partners. If you reside outside of the United States, you consent to having your personal data transferred to and processed in the United States. Privacy