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Hi Eric, I am planning to work on detecting such anomalities. What I have thought about so far the following approaches: - n-gram analysis - basket analysis - similarity detection of Solr - final state automat The tools I will use: Apache Solr and Apache Spark. I haven't started yet the implementation. Best, Péter 2017-10-25 17:57 GMT+02:00 Eric Lease Morgan <[log in to unmask]>: > Has anybody here played with any clustering techniques for normalizing bibliographic data? > > My bibliographic data is fraught with inconsistencies. For example, a publisher’s name may be recorded one way, another way, or a third way. The same goes for things like publisher place: South Bend; South Bend, IN; South Bend, Ind. And then there is the ISBD punctuation that is sometimes applied and sometimes not. All of these inconsistencies make indexing & faceted browsing more difficult than it needs to be. > > OpenRefine is a really good program for finding these inconsistencies and then normalizing them. OpenRefine calls this process “clustering”, and it points to a nice page describing the various clustering processes. [1] Some of the techniques included “fingerprinting” and calculating “nearest neighbors”. Unfortunately, OpenRefine is not really programable, and I’d like to automate much of this process. > > Does anybody here have any experience automating the process of normalize bibliographic (MARC) data? > > [1] about clustering - http://bit.ly/2izQarE > > — > Eric Morgan -- Péter Király software developer GWDG, Göttingen - Europeana - eXtensible Catalog - The Code4Lib Journal http://linkedin.com/in/peterkiraly