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The goal of the paper is to give an overview on the state of the art of data mining and text mining approaches which are useful for bibliometrics and patent databases. The paper explains the basics of data mining in a non-technical manner. Basic approaches from statistics and machine learning are introduced in order to clarify the groundwork of data mining methods. Text mining is introduced as a special case of data mining. Data and text mining applications especially useful for bibliometrics and querying of patent databases are reviewed and three case studies are described. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Stored only when both papers are in the corpus — 22 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.