On Validation of Clustering Techniques for Bibliographic Databases

Abstract

In entity name disambiguation, performance evaluation of any approach is difficult. This is due to the fact that correct or actual results are often not known. Generally for evaluation purpose, three measures namely precision, recall and f-measure are used. They all are external validity indices because they need golden standard data. But in Bibliographic databases like DBLP, Arnetminer, Scopus, Web of Science, Google Scholar, etc., gold standard data is not easily available and it is very difficult to obtain this due to the overlapping nature of data. So, there is a need to use some other matrices for evaluation purpose. In this paper, some internal cluster validity index based schemes are proposed for evaluating entity name disambiguation algorithms when applied on bibliographic data without using any gold standard datasets. Two new internal validity indices are also proposed in the current paper for this purpose. Experimental results shown on seven bibliographic datasets reveal that proposed internal cluster validity indices are able to compare the results obtained by different methods without prior/gold standard. Thus the present paper demonstrates a novel way of evaluating any entity matching algorithm for bibliographic datasets without using any prior/gold standard information.

PSOBER: PSO based entity resolution

“…Bic index was used by Tang et al to predict the number of clusters. A later work done by Mishra [28] consists of proposing two other cluster validity indices. Nevertheless, these indices were used to assess the quality of clustering only for bibliographic databases.…”

“…In order to assess the quality of a resulting partitioning, cluster validity indices [16,[25][26][27][28] are suggested by researchers community. These cluster validity indices permit to quantify and measure the quality of the obtained partitioning.…”