Directories provide a general mechanism for describing resources and enabling information sharing within and across organizations. Directories must resolve differing structures and vocabularies in order to communicate effectively, and interoperability of the directories is becoming increasingly important. This study proposes an approach that integrates a genetic algorithm with a neural network based clustering algorithm - Self-Organizing Maps (SOM) - to systematically cluster directory metadata, highlight similar structures, recognize developing patterns of practice, and potentially promote homogeneity among the directories. The proposed approach utilizes the computing power of Grid infrastructure to improve system performance. The study also explores the feasibility of automating the SOM clustering process in a converging domain by incrementally building a stable SOM map with respect to an initial reference set. Empirical investigations were conducted on sets of Lightweight Directory Access Protocol (LDAP) directory metadata. The experimental results show that the proposed approach can effectively and efficiently cluster LDAP directory metadata at the level of domain experts and a stable SOM map can be created for a set of converging LDAP directory metadata.

self-organizing maps, LDAP directory, clustering analysis, genetic algorithm, grid, reference set