Application of Agglomerative Hierarchical Clustering (AHC) for Grouping Prospective Scholarship Recipients

Scholarships are a form of financial aid provided to individuals to support their education. Scholarships can be granted by educational institutions, governments, organizations, or individuals as a means of assisting with educational expenses. Scholarships can take various forms, including cash grants, tuition waivers, assistance in purchasing books or educational supplies, or a combination of these forms of aid. Scholarship recipients are typically selected based on specific criteria such as academic excellence, financial need, special talents or interests, or achievements in a particular field. The current method of selecting scholarship recipients involves evaluating the characteristics of students who are deemed outstanding and actively engaged in the learning process. Due to the large number of students and limited scholarship quotas, this process often consumes a significant amount of time, and the selection of scholarship recipients may not be entirely accurate. To aid in the selection of scholarship recipients, data mining techniques are employed, specifically the clustering method. Agglomerative Hierarchical Clustering (AHC) is one of the clustering algorithms capable of grouping a set of data into specific data clusters. The data analyzed for the determination of scholarship recipients include academic performance data or grades, extracurricular activity data, economic data, and student achievement data. By using data mining and clustering methods, educational institutions or organizations offering scholarships can analyze the data of potential scholarship recipients to identify groups with similarities based on specific criteria. This approach can make the selection and grouping of scholarship candidates more efficient and objective.