Explains the KDD process, with an emphasis on the research area known as feature selection.
Written in 2004; 1,834 words; 6 sources; APA; $ 58.95
Paper Summary:
This paper explains KDD as the overall process of discovering useful knowledge from data and then goes on to describe the steps in this process. Emphasis is placed on feature selection, a popular research area in pattern recognition, statistics, and data mining communities.
From the Paper:
"Data mining is the application of specific algorithms for extracting structure from data. The additional steps in the KDD process, such as data preparation, data selection, data cleaning, incorporating appropriate prior knowledge, and proper interpretation of the results of mining, are essential to ensure that useful knowledge is derived from the data. Blind application of data mining methods (rightly criticized as "data dredging" in the statistical literature) can be a dangerous activity easily leading to discovery of meaningless patterns."
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