Patent Big Data Analysis using Fuzzy Learning
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Sangsung | - |
dc.contributor.author | Lee, Seung-Joo | - |
dc.contributor.author | Jun, Sunghae | - |
dc.date.accessioned | 2021-09-03T03:18:35Z | - |
dc.date.available | 2021-09-03T03:18:35Z | - |
dc.date.created | 2021-06-16 | - |
dc.date.issued | 2017-08 | - |
dc.identifier.issn | 1562-2479 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/82634 | - |
dc.description.abstract | Big data has had an immense effect on most social and industrial fields. It has three main characteristics, namely volume, variety, and velocity. Volume refers to the tremendous size of big data, variety pertains to its heterogeneous sources including numbers, text, and figures, and velocity refers to the rapid speed of data growth. Patent documents follow the characteristics of big data. A patent contains various results about the developed technology such as title, abstract, citations, figures, and drawings. In general, the volume of patent documents related to a target technology is very large. Moreover, a massive number of patent applications are submitted to the patent offices in every country daily. Patent data are analyzed for R&D planning by many institutes and companies. In this study, we propose a methodology for technology analysis applied to patent big data. Additionally, we employ fuzzy learning based on the fuzzy rule-based system for patent big data analysis. We study the fuzzy models for classification, regression, and clustering and group the patents by the fuzzy classification model. Using a fuzzy regression model, we build a technological relationship between subtechnologies. Lastly, we develop a fuzzy clustering model for technology clustering. To illustrate how our research may be applied to a practical domain, we employ a case study using the patent documents related to the three-dimensional printing technology. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER | - |
dc.subject | REGRESSION | - |
dc.subject | CLASSIFICATION | - |
dc.subject | ALGORITHMS | - |
dc.subject | SYSTEMS | - |
dc.subject | MODEL | - |
dc.title | Patent Big Data Analysis using Fuzzy Learning | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Park, Sangsung | - |
dc.identifier.doi | 10.1007/s40815-016-0192-y | - |
dc.identifier.scopusid | 2-s2.0-85027040625 | - |
dc.identifier.wosid | 000407380900017 | - |
dc.identifier.bibliographicCitation | INTERNATIONAL JOURNAL OF FUZZY SYSTEMS, v.19, no.4, pp.1158 - 1167 | - |
dc.relation.isPartOf | INTERNATIONAL JOURNAL OF FUZZY SYSTEMS | - |
dc.citation.title | INTERNATIONAL JOURNAL OF FUZZY SYSTEMS | - |
dc.citation.volume | 19 | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 1158 | - |
dc.citation.endPage | 1167 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Automation & Control Systems | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Automation & Control Systems | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.subject.keywordPlus | REGRESSION | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
dc.subject.keywordPlus | ALGORITHMS | - |
dc.subject.keywordPlus | SYSTEMS | - |
dc.subject.keywordPlus | MODEL | - |
dc.subject.keywordAuthor | Fuzzy classification | - |
dc.subject.keywordAuthor | Fuzzy regression | - |
dc.subject.keywordAuthor | Fuzzy clustering | - |
dc.subject.keywordAuthor | Technology analysis | - |
dc.subject.keywordAuthor | Patent | - |
dc.subject.keywordAuthor | Fuzzy learning | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.