Name
Abstract | ||
|---|---|---|
In today's business environment, competition within industries is becoming more and more intense. To survive in this fast-paced competitive environment, it's important to know what the core patents are and how the patents can be grouped. This study focuses on discovering core patents and clustering patents using a patent citation network in which core patents are represented as an influential node and patent groups as a cluster of nodes. Existing methods have discovered influential nodes and cluster nodes separately, especially in a citation network. This study develops a method used to detect influential nodes (that is, core patents) and clusters (that is, patent groups) in a patent citation network simultaneously rather than separately. The method allows a core patent in each patent group to be discovered easily and the distribution of similar patents around a core patent to be recognized. For this study, kernel k-means clustering with a graph kernel is introduced. A graph kernel helps to compute implicit similarities between patents in a high-dimensional feature space. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/MIS.2012.85 | IEEE Intelligent Systems |
Keywords | Field | DocType |
cluster nodes,patents,pattern clustering,patent group detection,business environment,core patent,implicit similarities,core patent detection,high-dimensional feature space,graph kernel approach,graph kernel,citation network,patent citation network,influential nodes,intelligent systems,graph theory,kernel k-means clustering,citation analysis,patent clustering,clustering algorithms,kernel,couplings | Kernel (linear algebra),Graph kernel,Cluster (physics),Data mining,Feature vector,Intelligent decision support system,Computer science,Tree kernel,Citation network,Cluster analysis | Journal |
Volume | Issue | ISSN |
29 | 4 | 1541-1672 |
Citations | PageRank | References |
2 | 0.37 | 13 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dohyun Kim | 1 | 2 | 0.37 |
| Bangrae Lee | 2 | 37 | 2.89 |
| Hyuck Jai Lee | 3 | 38 | 3.05 |
| Sang Pil Lee | 4 | 9 | 0.93 |
| Yeongho Moon | 5 | 12 | 1.98 |
| Myong Kee Jeong | 6 | 42 | 5.21 |