Redescription mining is a data analysis technique that has found applications in diverse fields. The most used redescription mining approaches involve two phases: finding matching pairs among data attributes and extending the pairs. This process is relatively efficient when the number of attributes remains limited and when the attributes are Boolean, but becomes almost intractable when the data consist of many numerical attributes. In this paper, we present new algorithms that perform the matching and extension orders of magnitude faster than the existing approaches. Our algorithms are based on locality-sensitive hashing with a tailored approach to handle the discretisation of numerical attributes as used in redescription mining.

翻译：重描述挖掘是一种已在多个领域得到应用的数据分析技术。最常用的重描述挖掘方法包含两个阶段：在数据属性间寻找匹配对，并对匹配对进行扩展。当属性数量有限且属性为布尔类型时，该过程相对高效；但当数据包含大量数值属性时，该过程几乎无法处理。本文提出了一系列新算法，其匹配与扩展速度比现有方法快数个数量级。我们的算法基于局部敏感哈希，并采用专门方法处理重描述挖掘中数值属性的离散化问题。