Patent Knowledge Discovery Using Data Analytics
| Autor: | Pranomkorn Ampornphan, Sutep Tongngam |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Strategic planning
Association rule learning Computer science business.industry 05 social sciences k-means clustering 02 engineering and technology Intellectual property 050905 science studies Data science Text mining Knowledge extraction 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 0509 other social sciences Everyday life business Cluster analysis |
| Zdroj: | Proceedings of the 2017 International Conference on Information Technology. |
| DOI: | 10.1145/3176653.3176721 |
| Popis: | Patents are a form of intellectual property that is close to most everyone. Generally, goods or appliance in everyday life are the inventions that provide the solutions to improve a specific technological problem or production process. The invention concepts that come from patent documents help to sustain comfortable and safety in human life. Also, patents are the representatives of technology detection for trends analysis and R&D strategic planning in organization. The main objective of this study is to apply data analytics to identify the relations among variables in patent documents using k-means clustering, association rules mining, and text mining approaches. The findings from clustering identified the prominent technological profiles. Association rules mining identified pattern associated within each technological profile. And text mining identified informative words related to invention concepts. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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