The problem is not just that there is a large amount of data, or that the goal is exploratory. For many exploratory statistical problems, the answers become clearer as the size of the data set become larger. Finding means and medians, as well as regression coefficients, fall into this easy category. But many problems in data mining have what might be called subtle structure , defined as those structures that are difficult to find even in large samples. Multivariate outlier detection and cluster finding fall into this category.
Subtle structure is often definable as the global optimum of a function with many local optima.
The best global optimum may be hard to find with small data sets, but good computational procedures with small data sets may be impractical with large ones. Multivariate data cleaning is more difficult, but is an essential step in a complete analysis. Data segmentation is here taken to mean the division of the data into nonoverlapping subsets that are relatively similar within a subset. Some points may be outliers, and so belong to no group. We do not assume that any reliable prior knowledge exists as to which points belong together, or even how many groups there may be.