Association Analysis of Alumni Giving: A Formal Concept Analysis

Abstract

A large sample (initially 33,000 cases representing a ten percent trial) of university alumni giving records for a large public university in the southwestern United States are analyzed by Formal Concept Analysis (FCA). This likely represents the initially attempt to perform analysis of such data by means of a machine learning technique. The variables employed include the gift amount to the university foundation (UF) as well as traditional demographic variables such as year of graduation, gender, ethnicity, marital status, etc, The UF serves as one of the institution's non-profit, fund-raising organizations. It pursues substantial gifts that are designated for the educational or leadership programs of the giver's choice. Although they process gifts of all sizes, the UF focus is on major gifts and endowments. The Association Analysis (AA) of the given dataset is a two-step process. In the first step, the data items that are frequently appear together (i.e. concepts) are systematically identified and in the second step, each concept is converted into a set of rules called association rules. The hypothesis examined in this paper is that the generosity of alumni toward his/her alma mater can be predicted using association rules obtained by applying the FCA approach.