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 is analyzed by Formal Concept Analysis. This likely represents the initial 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 as well as traditional demographic variables such as year of graduation, gender, ethnicity, marital status, etc. The foundation 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 foundation’s focus is on major gifts and endowments. Association Analysis of the given dataset is a two-step process. In the first step, FCA is applied to identify concepts and their relationships and in the second step, the association rules are defined for each concept. 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 Formal Concept Analysis approach.
Efficient Mining of Association Rules Based on Formal Concept Analysis
