Sociologists have persuasively argued that cultural meaning can be interpreted by analyzing the systems of relations that measure the so-called ‘going together’ of cultural materials. Research investigating cultural tastes and preferences has used this approach to interpret consumption patterns as relational systems using a variety of techniques including multidimensional scaling, two-mode network analysis, and variable correlation networks. I contribute to this growing set of tools by describing and demonstrating the use of a datamining technique with scant history of use within sociology, called ‘association-rules.’ The key contribution of this technique is that it generates directed relationships between variables (e.g., preference for opera → preference for ballet), which has several advantages over existing techniques that conceptualize relationality in terms of mutual presence. I show how such ‘one-sided’ clustering (A goes with B, but B may not go together with A) can be represented and analyzed as network graphs, an approach I call ‘Rulenet.’ I discuss how the proposed technique can provide relatively novel insights into the organizations of tastes, less feasible via other techniques, and illustrate Rulenet on two well-known cultural participation survey datasets for the United States: (1) The Survey of Public Participation in the Arts (SPPA) from 2017 and (2) The General Social Survey Culture Module from 1993 (GSS).

中文翻译：

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Rulenet：使用关联规则和网络图绘制文化偏好的结构

社会学家令人信服地认为，文化意义可以通过分析衡量所谓文化材料“走向”的关系系统来解释。调查文化品味和偏好的研究使用这种方法将消费模式解释为使用各种技术的关系系统，包括多维缩放、双模网络分析和可变相关网络。我通过描述和演示一种在社会学中几乎没有使用历史的数据挖掘技术（称为“关联规则”）来为这套不断增长的工具做出贡献。这种技术的主要贡献在于它在变量之间产生定向关系（例如，对歌剧的偏好→对芭蕾舞的偏好），与现有根据相互存在概念化关系的技术相比，这有几个优势。我展示了如何将这种“单边”聚类（A 与 B 一起，但 B 可能不与 A 一起）表示和分析为网络图，我称之为“规则网”。我讨论了所提出的技术如何为品味组织提供相对新颖的见解，而其他技术则不太可行，并在两个著名的美国文化参与调查数据集上说明了 Rulenet：（1） 2017 年的公众参与艺术调查 （SPPA） 和 （2） 1993 年的一般社会调查文化模块 （GSS）。