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.

本文介绍了一种基于局部敏感哈希的算法，用于处理纯数值属性的再描述挖掘，以实现更快速的匹配和扩展过程。

利用局部敏感哈希进行快速再描述挖掘

Fast Redescription Mining Using Locality-Sensitive Hashing

Machine learning (ML) has employed various discretization methods to
partition numerical attributes into intervals. However, an effective
discretization technique remains elusive in many ML applications, such as
association rule mining. Moreover, the existing discretization techniques do
not reflect best the impact of the independent numerical factor on the
dependent numerical target factor. This research aims to establish a benchmark
approach for numerical attribute partitioning. We conduct an extensive analysis
of human perceptions of partitioning a numerical attribute and compare these
perceptions with the results obtained from our two proposed measures. We also
examine the perceptions of experts in data science, statistics, and engineering
by employing numerical data visualization techniques. The analysis of collected
responses reveals that $68.7\%$ of human responses approximately closely align
with the values generated by our proposed measures. Based on these findings,
our proposed measures may be used as one of the methods for discretizing the
numerical attributes.

本研究通过收集人类和数据科学、统计学以及工程专家的反馈，分析了数值属性的分割，提出了两种度量方法，结果显示约 68.7% 的人类反馈与我们的度量方法一致，因此我们的度量方法可作为一种用于数值属性离散化的方法之一。