Overview

Many texts on data mining focus on algorithms. The truth is that in practical data mining, the algorithm isn't very important. So, be warned, don't get hung up on algorithms. But how can this be? "Surely," the reader is now commenting, "since algorithms figure so large in all of the books I've read on data mining and in all of the data mining courses I've taken, they must be important!" (The less charitable reader might even be thinking, "What? Does this guy really know what he's talking about?")

Algorithms are important for researchers in data mining who are attempting to develop new ways of mining data, and who are attempting to mine data that hasn't yet been mineable. Much leading edge work is going into developing techniques (including new algorithms and variations of existing algorithms) for mining Web data, pictures, text, spoken words, and many other types of data. Also, there are enormous challenges involved in developing algorithms that are even theoretically capable of mining huge data streams, such as that generated by switches of large telephone networks. For such folk, algorithms are indeed crucial.

However, for an analyst who needs to use the techniques of data mining to solve business problems, reasonably good algorithms have already been developed, taken out of the laboratory, wrapped in robust and reliable commercial packaging, tested for usability, and delivered with help screens, training manuals, tutorials, and instruction. When so wrapped, these are tools, not algorithms. Buried inside the tool are one or more...