How to Normalize Co-Occurrence Data? An Analysis of Some Well-Known Similarity Measures
In scientometric research, the use of co-occurrence data is very common. In many cases, a similarity measure is employed to normalize the data. However, there is no consensus among researchers on which similarity measure is most appropriate for normalization purposes. In this paper, we theoretically analyze the properties of similarity measures for co-occurrence data, focusing in particular on four well-known measures: the association strength, the cosine, the inclusion index, and the Jaccard index. We also study the behavior of these measures empirically. Our analysis reveals that there exist two fundamentally different types of similarity measures, namely set-theoretic measures and probabilistic measures. The association strength is a probabilistic measure, while the cosine, the inclusion index, and the Jaccard index are set-theoretic measures. Both our theoretical and our empirical results indicate that co-occurrence data can best be normalized using a probabilistic measure. This provides strong support for the use of the association strength in scientometric research.
|Keywords||Jaccard index, association strength, cosine, inclusion index, similarity measure|
|JEL||Econometric and Statistical Methods: Special Topics: Other (jel C49), Business Administration and Business Economics; Marketing; Accounting (jel M), Production Management (jel M11), Transportation Systems (jel R4)|
|Publisher||Erasmus Research Institute of Management|
|Series||ERIM Report Series Research in Management|
|Journal||ERIM report series research in management Erasmus Research Institute of Management|
van Eck, N.J.P, & Waltman, L. (2009). How to Normalize Co-Occurrence Data? An Analysis of Some Well-Known Similarity Measures (No. ERS-2009-001-LIS). ERIM report series research in management Erasmus Research Institute of Management. Erasmus Research Institute of Management. Retrieved from http://hdl.handle.net/1765/14528