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Mining hyperintervals Getting to grips with real-valued data

Many uses of data mining, such as clustering, classification, the construction of decision trees, subgroup discovery and itemset mining, often fail to be able to cope with real-valued data well. In fact, it is common for data mining methods to only work well on nominal data with little different values. We build the theory to fill this gap for data from arbitrary uncountable sets and introduce an efficient method to mine data, without the usual discretization as a pre-processing step. It is shown that discretization is not needed in order to make use of the MDL principle.