Research summary: This paper advances strategic management research by taking a close look at the reasons, procedures, and results of cluster identification methods, focusing on a density-based algorithm that organically define clusters from actual locations of economic activities. Despite being a popular research topic and analytical tool, geographic clusters are often studied with little consideration given to the underlying economic activities, the unique cluster boundaries, or the appropriate benchmark of economic concentration. Our goal is to increase awareness of the complexities behind cluster identification, and to provide concrete insights and methodologies applicable to various empirical settings. The method we propose is especially useful when researchers work in global settings, where data available at different geographic units complicates comparisons across countries. Managerial summary: Geographic proximity has been recognized as a fundamental factor driving firm performance, especially in knowledge-intensive industries. However, despite increasing interest in the study of geographic clusters—locations with a high concentration of economic activity—we as researchers have not given sufficient consideration to the underlying economic activity, the unique cluster boundaries, or even the definition of economic concentration. In this paper, we carefully examined the existing methodologies for cluster identification and proposed a method that defines clusters based on the actual location of economic activity. This new method is applicable to various empirical settings beyond geographic clusters. In addition, because clusters are defined by actual economic activity rather than administrative boundaries, it allows for meaningful comparison across countries.