That is, after running several algorithms on a dataset, they select the algorithm that performs best overall. Welge's group often take a hybrid approach. Rather than applying one algorithm to an entire problem, they split the problem into segments that can be attacked by whichever algorithm is most suitable to each segment. This approach is similar to hiring different chefs to prepare each course in a seven-course meal rather than relying on the pastry chef to also master the bouillabaisse. Sometimes solving a problem requires a mix of commercial software and customized code.
Companies hesitate to abandon the computing systems they've pieced together over decades.
Nor do they always have staff skilled in the programming languages of newer codes. Companies want a system that not only finds the information they seek but is also easy to maintain. When businesses first apply data mining techniques to a problem, they are likely to get a big bang for their buck from generic programs. Only when they need better performance will they reap benefits from Welge's custom fit. For further information, contact Michael Welge .