Discretization algorithms serve as a critical pre-processing step within data mining and machine learning, transforming continuous attributes into discrete categories to enhance the interpretability ...

Incomplete data affects classification accuracy and hinders effective data mining. The following techniques are effective for working with incomplete data. The ISOM-DH model handles incomplete data ...

Predictive analytics enables you to develop mathematical models to help you better understand the variables driving success. Predictive analytics relies on formulas that compare past successes and ...

Stanford (Calif.) Medicine researchers created an algorithm that uses artificial intelligence to sift through EHR data for implantable medical device surveillance, according to the medical school’s ...

The visual data mining process, seen in the first part of this two-part article, revealed patterns in four dimensions between cumulative gas well production and independent variables. "Jump ...

Jeremy Kun does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their ...

Data mining is more about process than it is about clever algorithms. When the process is not well understood, all the clever techniques and algorithms can get applied to the wrong data, in the wrong ...

In an era when data is everything, everywhere, all at once and computation has almost no limit, ensuring privacy while leveraging data analytics is paramount. The US Department of Commerce’s National ...