Computational Science and Data Mining

摘要

The last decade has witnessed an impressive growth of Data Mining through algorithms and applications. Despite the advances, a computational theory of Data Mining is still largely outstanding. This paper discusses some aspects relevant to computation in Data Mining from the point of view of the Machine Learning theoretician. Computational techniques used in other fields that deal with learning from data, such as Statistics and Machine Learning, are potentially very relevant. However, the specifics of Data Mining are such that most often those techniques are not directly applicable but require to be re-cast and re-analysed within Data Mining starting from first principles. We illustrate this with a PAC-learnability analysis for a Data Mining-like task. We show that accounting for Data Mining specific requirements, such as inference of weak predictors and agnosticity assumptions, requires the generalisation of the classical PAC framework in novel ways.