A genetic algorithm based approach for systematic SOM clustering of directory metadata

Directories play an important role in describing resources and enabling information sharing within and among organizations. To communicate effectively, directories must resolve differing structures and vocabularies. This paper proposes a systematic approach to address the interoperability of directories. The approach couples 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 ultimately promote homogeneity among the directories. To evaluate the effectiveness of the proposed approach, an experiment on Lightweight Directory Access Protocol (LDAP) directory metadata is conducted. The experimental results show that a genetic algorithm can discover parameter values for a SOM algorithm such that the computer clustering results are comparable to that of domain experts. The proposed approach provides an effective mechanism to systematically cluster directory metadata and promote homogeneity among them.