Improving Data Integrity of Individual-based Bibliographic Repository Using Clustering Techniques

Abstract

This paper presents a method to improve data integrity of individual-based bibliographic repository. Integrity improvement is done by comparing individual-based publication raw data with individual-based clustered publication data. Hierarchical Agglomerative Clustering is used to cluster the publication data with similar author names. Clustering is done by two steps of clustering. The first clustering is based on the co-author relationship and the second is by title similarity and year difference. The two-step hierarchical clustering technique for name disambiguation has been applied to Universitas Sriwijaya Publication Data Center with good accuracy.

Author Matching Classification with Anomaly Detection Approach for Bibliomethric Repository Data

“…DL content and service quality are strongly influenced by the ambiguity problem of the author's name in the citation and are considered as one of the most difficult problems faced by digital library researchers [3]. AND becomes a problem when a set of publication notes contains the name of the author which gives rise to more than one interpretation, ie the same author can appear with a different name [4] [5]. This becomes a point that reduces the quality of information and also reduces the reliability of the information because it impacts the information on the author, organization and other things that are displayed as part of the publication's notes [6].…”