Name
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. |
Year | DOI | Venue |
|---|---|---|
2017 | https://doi.org/10.1007/s40815-016-0192-y | International Journal of Fuzzy Systems |
Keywords | Field | DocType |
Fuzzy classification,Fuzzy regression,Fuzzy clustering,Technology analysis,Patent,Fuzzy learning | Fuzzy regression model,Data mining,Fuzzy clustering,Fuzzy classification,Fuzzy logic,Cluster analysis,Big data,Mathematics,Fuzzy rule,Fuzzy learning | Journal |
Volume | Issue | ISSN |
19 | 4 | 1562-2479 |
Citations | PageRank | References |
1 | 0.35 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sang-Sung Park | 1 | 80 | 7.25 |
| Seung Joo Lee | 2 | 18 | 4.43 |
| Sung-Hae Jun | 3 | 95 | 11.79 |