Baryła, M and Pociecha, J (2019). Euclidean distance as a measure of conformity to Benford's law in digital analysis for fraud detection. Book of Short Papers, Proceedings of the 12th Scientific Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society (SIS), pp. 7578.
This work cites the following items of the Benford Online Bibliography:
Benford, F (1938). The law of anomalous numbers. Proceedings of the American Philosophical Society, Vol. 78, No. 4 (Mar. 31, 1938), pp. 551572.





Cho, WKT and Gaines, BJ (2007). Breaking the (Benford) law: Statistical fraud detection in campaign finance. American Statistician 61(3), pp. 218223. ISSN/ISBN:00031305. DOI:10.1198/000313007X223496.





Joenssen, DW (2015). BenfordTests: Statistical Tests for Evaluating Conformity to Benford's Law. R package version 1.2.0 .





Morrow, J (2014). Benfordâ€™s Law, Families of Distributions and a Test Basis. Center for Economic Performance Discussion Paper No 1291.





Newcomb, S (1881). Note on the frequency of use of the different digits in natural numbers. American Journal of Mathematics 4(1), pp. 3940. ISSN/ISBN:00029327. DOI:10.2307/2369148.





Nigrini, MJ (2012). Benford's Law: Applications for Forensic Accounting, Auditing, and Fraud Detection . John Wiley & Sons: Hoboken, New Jersey. ISSN/ISBN:9781118152850. DOI:10.1002/9781119203094.




