Data Mining is used to discover patterns and relationships in data, with an emphasis on large observational data bases. It sits at the common frontiers of several fields including Data Base Management, Arti cial Intelligence, Machine Learning, Pattern Recognition, and Data Visualization. From a statistical perspective it can be viewed as computer automated exploratory data analysis of (usually) large complex data sets. In spite of (or perhaps because of) the somewhat exaggerated hype, this eld is having a major impact in business, industry, and science. It also a ords enormous research opportunities for new methodological developments. Despite the obvious connections between data mining and statistical data analysis, most of the methodologies used in Data Mining have so far originated in fields other than Statistics. This paper explores some of the reasons for this, and why statisticians should have an interest in Data Mining. It is argued that Statistics can potentially have a major in uence on Data Mining, but in order to do so some of our basic paradigms and operating principles may have to be modified. Data Mining and Statistics: What is the Connection?