Evaluating the use of clustering for automatically organising digital library collections

Authors:
Mark Hall;Paul Clough;Mark Stevenson
Affiliations:
Department for Computer Science, Sheffield University, Sheffield, Unformation School, Sheffield University, Sheffield, UK;Information School, Sheffield University, Sheffield, UK;Department for Computer Science, Sheffield University, Sheffield, UK
Venue:
TPDL'12 Proceedings of the Second international conference on Theory and Practice of Digital Libraries
Year:
2012

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Abstract

Large digital libraries have become available over the past years through digitisation and aggregation projects. These large collections present a challenge to the new user who wishes to discover what is available in the collections. Subject classification can help in this task, however in large collections it is frequently incomplete or inconsistent. Automatic clustering algorithms provide a solution to this, however the question remains whether they produce clusters that are sufficiently cohesive and distinct for them to be used in supporting discovery and exploration in digital libraries. In this paper we present a novel approach to investigating cluster cohesion that is based on identifying instruders in a cluster. The results from a human-subject experiment show that clustering algorithms produce clusters that are sufficiently cohesive to be used where no (consistent) manual classification exists.