We live in an information-heavy age, where trends and enlightening conclusions can be buried in the mountains of data — if only we know how to find them.
Monte Hancock, the mentor for the MS in Computer Science at Webster University’s campus location in Melbourne, Fla., can help.
His new book, Practical Data Mining, provides a road map for those who want to plan and execute a data mining project, but aren’t sure how to go about it.
If the term “data mining” is not familiar, Merriam-Webster defines it as “the practice of searching through large amounts of computerized data to find useful patterns or trends.”
“Everyone who has data is looking for ways to get the information out, and sometimes it is hidden in complex patterns,” Hancock says. “That’s when data mining enters the picture.”
“This technology can be used for a vast array of needs, such as seeking out predatory behavior on the internet, picking a stock, gathering homeland security information or targeting advertising to a specific audience.”
In addition to being an adjunct professor for Webster’s Melbourne campus location, Hancock is the chief scientist for Celestech, Inc. — an example of Webster faculty who bring a mix of global scholarship and professional experience to the classroom.
He has presented papers worldwide, notably in the areas of applied human factors and ergonomics and data mining. He currently has a patent pending for “distributed hierarchical machine reasoning architecture.”