A comparative study on the influence of similarity measures in hierarchical clustering in complex distributed object-oriented databases

Authors:
Adrian Sergiu Darabant;Horea Todoran;Octavian Cret;George Chis
Affiliations:
Dept. of Computer Science, Babes Bolyai University, Technical University, Cluj Napoca, Romania;Dept. of Computer Science, Babes Bolyai University, Technical University, Cluj Napoca, Romania;Dept. of Computer Science, Babes Bolyai University, Technical University, Cluj Napoca, Romania;Dept. of Computer Science, Babes Bolyai University, Technical University, Cluj Napoca, Romania
Venue:
ICCOMP'06 Proceedings of the 10th WSEAS international conference on Computers
Year:
2006

Citing 5
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Abstract

Class fragmentation is an essential phase in the design of Distributed Object Oriented Databases (DOODB). Due to their semantic similarity with the purpose of database fragmentation (obtaining sets of similar objects with respect to the user applications running in the system), clustering algorithms have recently begun to be investigated in the process of database fragmentation. This work proposes a study on the impact of different similarity measures applied in hierarchical agglomerative clustering algorithms for horizontal fragmentation of classes with complex attributes. This study would eventually help finding formal, automatic, approaches in choosing a particular similarity measure in accordance with: the applied clustering algorithm, the structure of the database inheritance/aggregation hierarchies, the semantics of data, etc.