Deriving insights from high-dimensional data is one of the core problems in
data mining. The difficulty mainly stems from the fact that there are
exponentially many variable combinations to potentially consider, and there are
infinitely many if we consider weighted combinations, even for linear
combinations. Hence, an obvious question is whether we can automate the search
for interesting patterns and visualizations. In this paper, we consider the
setting where a user wants to learn as efficiently as possible about
real-valued attributes. For example, to understand the distribution of crime
rates in different geographic areas in terms of other (numerical, ordinal
and/or categorical) variables that describe the areas. We introduce a method to
find subgroups in the data that are maximally informative (in the formal
Information Theoretic sense) with respect to a single or set of real-valued
target attributes. The subgroup descriptions are in terms of a succinct set of
arbitrarily-typed other attributes. The approach is based on the Subjective
Interestingness framework FORSIED to enable the use of prior knowledge when
finding most informative non-redundant patterns, and hence the method also
supports iterative data mining.

该研究介绍了一种基于主观趣味性框架的方法，用于在高维数据中查找最具信息量的子组。该方法可以同时考虑多个实值目标属性，并且通过迭代数据挖掘支持知识的应用。

基于实数目标的主观有趣子群发现

Subjectively Interesting Subgroup Discovery on Real-valued Targets

This paper presents a theory of error in cross-validation testing of
algorithms for predicting real-valued attributes. The theory justifies the
claim that predicting real-valued attributes requires balancing the conflicting
demands of simplicity and accuracy. Furthermore, the theory indicates precisely
how these conflicting demands must be balanced, in order to minimize
cross-validation error. A general theory is presented, then it is developed in
detail for linear regression and instance-based learning.

本文探讨了预测实值属性算法的交叉验证测试误差理论，理论表明预测实值属性需要在简化和准确性之间取得平衡，并明确说明了如何达到平衡以最小化交叉验证误差。作者提出了一个通用理论，并为线性回归和基于实例的学习进行了详细阐述。