Improving the accuracy of the author name disambiguation by using clustering ensemble

Today, digital libraries are important academic resources including millions of citations and bibliographic essential information such as titles, author's names and location of publications. From the view of knowledge accumulation management, the ability to search fast, accurate, desired contents, has a great importance. The complexity and similarity in these resources cause many challenges and ambiguities. One of the most of these challenges is the author name disambiguation which makes an extensive scope of research. Although many effective methods have been developed by using clustering techniques in disambiguation of the author's name, the accuracy of these methods is not acceptable and still there are some problems such as fragmentation and error in the produced results of these methods, since there is no uniform standard of citations, various combinations, and numerous, written, verbal patterns. In fact, experiences have shown that the use of a single method to disambiguate names does not provide results with a high accuracy despite concerns expressed above. In this paper, a new method is proposed to disambiguate author names in different formats and combinations with more accuracy. The proposed solution carries out the disambiguation in two steps; In the first step, agglomerative hierarchical clustering algorithm produces clusters using similar functions and different thresholds. In the second step, clusters produced by clustering ensemble technique in the previous stage are combined to provide more accurate clusters with less fragmentation. The proposed method is experimentally evaluated by conducted DBLP datasets with K criterion. The evaluation results show that the proposed method enhances the accuracy of disambiguation of author names in different formats.