Data Mining can be described, simply, as the extraction of useful information from large amounts of data. Many large companies are looking for more ways to mine their sales data, product data or even web site traffic data to find information that will allow them to become more profitable or maybe even just better at what they do.
Some well-understood examples of things that can be accomplished with data mining include Google News, which uses a variety of clustering and classification mechanisms to assemble news information, to Amazon.com’s recommendations.
It’s still true, however, that much of the writing about Data Mining techniques and concepts is extremely math-heavy. Most Data Mining books are texts for university-level courses. One has to look far and wide for straight-forward, easy-to-comprehend descriptions of these techniques. This session will attempt to introduce some of the most popular Data Mining concepts using no more mathematical formulas than absolutely necessary.